Role centers on designing, building, and maintaining cloud-based data pipelines for a fast-growing payments environment. You will partner with data analysts, product teams, and business...
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Role centers on designing, building, and maintaining cloud-based data pipelines for a fast-growing payments environment. You will partner with data analysts, product teams, and business stakeholders to ensure high-quality, reliable data drives decisions. The stack is cloud-first, with GCP and BigQuery, plus Hadoop, Spark, and Hive for legacy workloads. You will design ETL pipelines, data models, and integration processes to support reporting and analytics across large, cross-border datasets. The pitch promises scale and reliability, but the description omits governance, security, observability, and real data quality discipline. Core requirements are strong SQL and at least one of Python, Java, or Scala, plus experience with ETL frameworks and data warehousing. It sits at the analytics-engineering seam; whether that is a strength or buzzword fluff depends on how concrete the project scope proves to be in practice.
Smile advertises a Data Analyst role embedded in a European open-source powerhouse of 1,800 people across nine countries. The core duties read like a complete data lifecycle: extract, clean, and...
read more »
Smile advertises a Data Analyst role embedded in a European open-source powerhouse of 1,800 people across nine countries. The core duties read like a complete data lifecycle: extract, clean, and prepare data; analyze for trends; build dashboards; define KPIs; and partner with business teams to deliver usable insights. Technically, you’re in an OSS-heavy stack: ETL/ELT and Big Data with Spark/Hadoop, cloud platforms (AWS, Azure, GCP), orchestration (Kubernetes, Docker), BI tools (Power BI, Tableau), SQL/NoSQL, programming in Python/Java/Scala/R, API work, CI/CD, and search engines Elasticsearch or Solr. The breadth is its selling point and its leash: a Data Analyst who also touches data engineering, backend work, and even search optimization risks role creep and dilution of depth. Still, you gain broad tooling exposure, practical enterprise challenges, and a clear path to cross-disciplinary credibility, not buzzword bingo.
Masthead is a data reliability platform built for Google Cloud, focused on detecting anomalies and ensuring smooth data pipeline operations. It offers real-time notifications for data issues and pipeline errors without direct access to your sensitive data.
Client Engagement Analytics, Vice President
@ morgan-stanley
US | 2025-12-27
| USD
110000 - 190000
/ year
Morgan Stanley's Wealth Management Analytics & Data team seeks a VP to handle client engagement analytics with a focus on automation and strategic insights. A key differentiator is the emphasis on...
read more »
Morgan Stanley's Wealth Management Analytics & Data team seeks a VP to handle client engagement analytics with a focus on automation and strategic insights. A key differentiator is the emphasis on AI and automation-driven solutions; a risk involves relying on automated tools without proper oversight given the high-profile nature of reporting. The role demands extensive data experience in finance, proficiency in SQL, Python, Tableau, and familiarity with tools like Dataiku and Snowflake, with a salary between 110,000 and 190,000. The candidate will work with multiple teams, often remotely, in an environment that values independence and accuracy.
Client Engagement Analytics, Vice President
@ morgan-stanley
US | 2025-12-27
| USD
110000 - 190000
/ year
Morgan Stanley seeks a data professional with at least 7 years of experience, proficient in SQL, Python, and data visualization, to support client engagement analytics within wealth management....
read more »
Morgan Stanley seeks a data professional with at least 7 years of experience, proficient in SQL, Python, and data visualization, to support client engagement analytics within wealth management. The role involves ownership of reporting and insights in marketing campaigns, with a focus on automation and data storytelling—a notable advantage in this competitive field. A key risk is the dependence on complex data infrastructure, which may pose challenges if systems or collaboration falter.
Data Analyst - Business Intelligence, Regional Logistics
@ shopee
SG | 2025-12-27
Shopee’s BI Data Analyst posting reads like a hybrid: deep analytics plus data engineering chores, spanning from dashboards to building pipelines for logistics across multiple markets. The role...
read more »
Data Analyst - Business Intelligence, Regional Logistics
shopee
(SG) FULL TIME | JOB LISTED
Shopee’s BI Data Analyst posting reads like a hybrid: deep analytics plus data engineering chores, spanning from dashboards to building pipelines for logistics across multiple markets. The role promises cross-functional bite with Data Science/Engineering and Product, consistent with a data platform that must scale. Core tech stack expectations land on SQL and Python, with big-data tools such as Hadoop, Hive SQL, and Spark, plus ETL, Tableau and Looker for visual storytelling. The tension is real: you’re asked to translate qualitative and quantitative signals into actionable plans, while explaining technical concepts to non-tech stakeholders. It’s a decent all-rounder grind in a growing ecommerce operation; the risk is scope creep and ambiguity around success metrics and career ladders. Salary isn’t disclosed, which is a yellow flag in a market that still cares about pay transparency. Opportunities exist for hands-on impact in logistics optimization and cross-market scale.
Data Analyst - Business Intelligence, Marketplace Operations
@ shopee
SG | 2025-12-27
Shopee’s Regional Marketplace BI role asks a hands-on analyst to stitch SQL and Python into dashboards, data pipelines, and playbooks across multiple markets. You’ll chop large datasets, run ETL,...
read more »
Data Analyst - Business Intelligence, Marketplace Operations
shopee
(SG) FULL TIME | JOB LISTED
Shopee’s Regional Marketplace BI role asks a hands-on analyst to stitch SQL and Python into dashboards, data pipelines, and playbooks across multiple markets. You’ll chop large datasets, run ETL, and ship metrics that drive decisions from fraud to operations, while coordinating with product, data science, and regional teams. The tech footprint is solid but not glamorous: Hadoop/Hive, Spark, Tableau or Looker, and room to apply Generative AI for automation and insights. The challenge is real: balancing fast, operational reporting with robust data models, communicating nuanced findings to non‑technical stakeholders, and keeping up with cross‑market governance. Opportunities hinge on influencing policy and process at scale, plus hands-on exposure to AI-enabled analytics. In a crowded BI market, the role stands out if you value practical impact over buzz and prefer breadth to depth.
Masthead is a data reliability platform built for Google Cloud, focused on detecting anomalies and ensuring smooth data pipeline operations. It offers real-time notifications for data issues and pipeline errors without direct access to your sensitive data.
From the posting, the role is a global marketing analytics-support position that builds reporting and data-extraction solutions with SQL, Python, and Spark, atop a Hadoop ecosystem. It promises...
read more »
From the posting, the role is a global marketing analytics-support position that builds reporting and data-extraction solutions with SQL, Python, and Spark, atop a Hadoop ecosystem. It promises actionable insights to steer campaigns, with collaboration across offices and a data-engineering safety net. The tech stack hints at a legacy‑leaning big data setup rather than a cloud-native analytics dream, and the emphasis on reporting work suggests heavy ETL and dashboard duties more than true engineering or data science. Expect to wrangle messy marketing data, manage evolving tracking needs, and juggle cross‑team priorities in a fast-paced environment. The absence of salary details and a clear ownership model makes the opportunity feel promising in title but murky in scale and career progression.
Junior Data Analyst - Business Intelligence, Regional Operations
@ shopee
SG | 2025-12-26
Shopee's Regional Operations role promises hands-on BI work across big data, building dashboards and pipelines to measure day-to-day efficiency and strategic impact. The job requires SQL and...
read more »
Junior Data Analyst - Business Intelligence, Regional Operations
shopee
(SG) FULL TIME | JOB LISTED
Shopee's Regional Operations role promises hands-on BI work across big data, building dashboards and pipelines to measure day-to-day efficiency and strategic impact. The job requires SQL and Python, plus familiarity with Hadoop, Hive, Spark, ETL tools, and visualization platforms like Tableau or Looker. It asks for 0-3 years and a business analytics or STEM degree, which is typical, but the real work is data infrastructure alongside reporting, converting qualitative and quantitative inputs into scalable playbooks. The environment is cross-market and cross-functional, so stakeholder management and translating tech concepts into actionable plans matter as much as modelling. The risks include a heavy emphasis on data engineering tasks under a BI banner, potential tooling churn, and the need to navigate regional data governance. Opportunities lie in end-to-end ownership of metrics and practical impact on operations, not just dashboards.
Wealth Data, Analytics & Innovation - Business Analyst - Associate
@ citi
US | 2025-12-25
| USD
86330 - 120470
/ year
A role requiring collaboration across teams to develop personalized engagement platforms, with a focus on AI and decision engines; the differentiator lies in integrating AI solutions into...
read more »
Wealth Data, Analytics & Innovation - Business Analyst - Associate
citi
(US) FULL TIME | JOB LISTED
A role requiring collaboration across teams to develop personalized engagement platforms, with a focus on AI and decision engines; the differentiator lies in integrating AI solutions into financial services, but there's a risk of misalignment between stakeholder requirements and technical execution. The salary range is explicitly provided as $86 330,00 - $120 470,00, with potential bonuses and benefits. The position involves data tools like SQL, data lakes, ETLs, Hadoop, Snowflake, and personalization engines, and is categorized as a business analyst role. It is not an intermediary position.
Mastercard seeks a Performance Analytics consultant who leverages data and technology solutions to craft strategic insights for clients across various industries, integrating proprietary platforms...
read more »
Mastercard seeks a Performance Analytics consultant who leverages data and technology solutions to craft strategic insights for clients across various industries, integrating proprietary platforms with traditional management consulting. The role’s differentiator is the combination of Mastercard’s rich data assets with strategic advisory, though the risk of handling sensitive financial data cannot be ignored. Candidates must manage complex projects and client relationships, with no explicit salary details provided.
Consultant Bi / Expert tableau - H/F
@ scaliangroup
FR | 2025-12-24
SCALIAN sells itself as a top French engineering consultant with CAC40 clients and a 5,500-strong multinational footprint; the data-visualization role reads more like a tooling specialization than...
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SCALIAN sells itself as a top French engineering consultant with CAC40 clients and a 5,500-strong multinational footprint; the data-visualization role reads more like a tooling specialization than a data platform revolution. You’ll be a BI/Tableau expert embedded in a cross‑functional team, shaping requirements, KPIs, and dashboards while auditing and tuning reports for performance. The stack leans hard on Tableau, with Power BI and Cognos, plus Python, SQL, and JavaScript, plus a Hadoop ecosystem (Hue, Hive, Zeppelin). Training, knowledge sharing, and Agile rituals are promised, and there’s work on proposals and knowledge transfer at mission end. Pros: strong brand, formal processes, formal onboarding, extensive toolset exposure. Cons: tool-centric, potential vendor lock-in, opaque salary details, and consulting cadence that can eat time. If you want a big-firm lab to sharpen Tableau chops, this is plausible; if you crave a tool-agnostic data engineering pace, look elsewhere.
The role requires supporting marketing functions with data and insight, using SQL, Python, and Spark, while working with big data in the Hadoop ecosystem. The job emphasizes problem-solving,...
read more »
The role requires supporting marketing functions with data and insight, using SQL, Python, and Spark, while working with big data in the Hadoop ecosystem. The job emphasizes problem-solving, attention to detail, and collaboration with cross-functional teams. While the skills align with data engineering, the focus on marketing analytics and the inclusion of VBA/Spark add a unique dimension. The role stands out for its practical focus on real-world data challenges, though the emphasis on marketing may limit its appeal to purely data engineering audiences. The salary is not explicitly provided, and the tools list is sparse but includes relevant technologies.
Morgan Stanley's AVP, Client Engagement Analytics sits at the crossroads of data engineering, business intelligence, and strategic influence within Wealth Management. It’s a hands-on role: tame...
read more »
Morgan Stanley's AVP, Client Engagement Analytics sits at the crossroads of data engineering, business intelligence, and strategic influence within Wealth Management. It’s a hands-on role: tame disparate data, build end-to-end dashboards from sourcing to publishing, and deliver actionable insights that move the needle on client engagement and ROA. Expect to code in SQL and Python, visualize in Tableau, and work with Snowflake, Dataiku, Hue and Salesforce; establishing a Data Mart to deliver a single version of truth is central, with QA and risk controls baked in. Collaboration across Sales, Strategy, Product, Finance, and Technology is constant, and leadership must be persuaded with crisp storytelling. The pay is solid for the market, but bureaucracy and governance slow results; a genuine platform for impact, minus the buzzword bingo.
Masthead is a data reliability platform built for Google Cloud, focused on detecting anomalies and ensuring smooth data pipeline operations. It offers real-time notifications for data issues and pipeline errors without direct access to your sensitive data.
This role presents a challenge for a data engineer seeking to deliver meaningful value in a fast-paced, multi-platform environment. The position requires deep technical proficiency in SQL, Python,...
read more »
This role presents a challenge for a data engineer seeking to deliver meaningful value in a fast-paced, multi-platform environment. The position requires deep technical proficiency in SQL, Python, and big data technologies, with a focus on ETL pipeline design and maintenance. While the job description highlights strong analytical and communication skills, it lacks specific details about the company’s culture, team dynamics, or the extent of responsibility in a real-world setting. The role is technically demanding but lacks clarity on the company’s approach to innovation and collaboration. The opportunity lies in working with a large, dynamic organization that values technical excellence, but the description does not fully convey the nuances of the environment or the potential for growth within the team.
Role centers on designing, building, and maintaining cloud-based data pipelines for a fast-growing payments environment. You will partner with data analysts, product teams, and business stakeholders to ensure high-quality, reliable data drives decisions. The stack is cloud-first, with GCP and BigQuery, plus Hadoop, Spark, and Hive for legacy workloads. You will design ETL pipelines, data models, and integration processes to support reporting and analytics across large, cross-border datasets. The pitch promises scale and reliability, but the description omits governance, security, observability, and real data quality discipline. Core requirements are strong SQL and at least one of Python, Java, or Scala, plus experience with ETL frameworks and data warehousing. It sits at the analytics-engineering seam; whether that is a strength or buzzword fluff depends on how concrete the project scope proves to be in practice.
Smile advertises a Data Analyst role embedded in a European open-source powerhouse of 1,800 people across nine countries. The core duties read like a complete data lifecycle: extract, clean, and prepare data; analyze for trends; build dashboards; define KPIs; and partner with business teams to deliver usable insights. Technically, you’re in an OSS-heavy stack: ETL/ELT and Big Data with Spark/Hadoop, cloud platforms (AWS, Azure, GCP), orchestration (Kubernetes, Docker), BI tools (Power BI, Tableau), SQL/NoSQL, programming in Python/Java/Scala/R, API work, CI/CD, and search engines Elasticsearch or Solr. The breadth is its selling point and its leash: a Data Analyst who also touches data engineering, backend work, and even search optimization risks role creep and dilution of depth. Still, you gain broad tooling exposure, practical enterprise challenges, and a clear path to cross-disciplinary credibility, not buzzword bingo.
Morgan Stanley's Wealth Management Analytics & Data team seeks a VP to handle client engagement analytics with a focus on automation and strategic insights. A key differentiator is the emphasis on AI and automation-driven solutions; a risk involves relying on automated tools without proper oversight given the high-profile nature of reporting. The role demands extensive data experience in finance, proficiency in SQL, Python, Tableau, and familiarity with tools like Dataiku and Snowflake, with a salary between 110,000 and 190,000. The candidate will work with multiple teams, often remotely, in an environment that values independence and accuracy.
Morgan Stanley seeks a data professional with at least 7 years of experience, proficient in SQL, Python, and data visualization, to support client engagement analytics within wealth management. The role involves ownership of reporting and insights in marketing campaigns, with a focus on automation and data storytelling—a notable advantage in this competitive field. A key risk is the dependence on complex data infrastructure, which may pose challenges if systems or collaboration falter.
Data Analyst - Business Intelligence, Regional Logistics
shopee
(SG) FULL TIME | JOB LISTED
Shopee’s BI Data Analyst posting reads like a hybrid: deep analytics plus data engineering chores, spanning from dashboards to building pipelines for logistics across multiple markets. The role promises cross-functional bite with Data Science/Engineering and Product, consistent with a data platform that must scale. Core tech stack expectations land on SQL and Python, with big-data tools such as Hadoop, Hive SQL, and Spark, plus ETL, Tableau and Looker for visual storytelling. The tension is real: you’re asked to translate qualitative and quantitative signals into actionable plans, while explaining technical concepts to non-tech stakeholders. It’s a decent all-rounder grind in a growing ecommerce operation; the risk is scope creep and ambiguity around success metrics and career ladders. Salary isn’t disclosed, which is a yellow flag in a market that still cares about pay transparency. Opportunities exist for hands-on impact in logistics optimization and cross-market scale.
Data Analyst - Business Intelligence, Marketplace Operations
shopee
(SG) FULL TIME | JOB LISTED
Shopee’s Regional Marketplace BI role asks a hands-on analyst to stitch SQL and Python into dashboards, data pipelines, and playbooks across multiple markets. You’ll chop large datasets, run ETL, and ship metrics that drive decisions from fraud to operations, while coordinating with product, data science, and regional teams. The tech footprint is solid but not glamorous: Hadoop/Hive, Spark, Tableau or Looker, and room to apply Generative AI for automation and insights. The challenge is real: balancing fast, operational reporting with robust data models, communicating nuanced findings to non‑technical stakeholders, and keeping up with cross‑market governance. Opportunities hinge on influencing policy and process at scale, plus hands-on exposure to AI-enabled analytics. In a crowded BI market, the role stands out if you value practical impact over buzz and prefer breadth to depth.
From the posting, the role is a global marketing analytics-support position that builds reporting and data-extraction solutions with SQL, Python, and Spark, atop a Hadoop ecosystem. It promises actionable insights to steer campaigns, with collaboration across offices and a data-engineering safety net. The tech stack hints at a legacy‑leaning big data setup rather than a cloud-native analytics dream, and the emphasis on reporting work suggests heavy ETL and dashboard duties more than true engineering or data science. Expect to wrangle messy marketing data, manage evolving tracking needs, and juggle cross‑team priorities in a fast-paced environment. The absence of salary details and a clear ownership model makes the opportunity feel promising in title but murky in scale and career progression.
Junior Data Analyst - Business Intelligence, Regional Operations
shopee
(SG) FULL TIME | JOB LISTED
Shopee's Regional Operations role promises hands-on BI work across big data, building dashboards and pipelines to measure day-to-day efficiency and strategic impact. The job requires SQL and Python, plus familiarity with Hadoop, Hive, Spark, ETL tools, and visualization platforms like Tableau or Looker. It asks for 0-3 years and a business analytics or STEM degree, which is typical, but the real work is data infrastructure alongside reporting, converting qualitative and quantitative inputs into scalable playbooks. The environment is cross-market and cross-functional, so stakeholder management and translating tech concepts into actionable plans matter as much as modelling. The risks include a heavy emphasis on data engineering tasks under a BI banner, potential tooling churn, and the need to navigate regional data governance. Opportunities lie in end-to-end ownership of metrics and practical impact on operations, not just dashboards.
Wealth Data, Analytics & Innovation - Business Analyst - Associate
citi
(US) FULL TIME | JOB LISTED
A role requiring collaboration across teams to develop personalized engagement platforms, with a focus on AI and decision engines; the differentiator lies in integrating AI solutions into financial services, but there's a risk of misalignment between stakeholder requirements and technical execution. The salary range is explicitly provided as $86 330,00 - $120 470,00, with potential bonuses and benefits. The position involves data tools like SQL, data lakes, ETLs, Hadoop, Snowflake, and personalization engines, and is categorized as a business analyst role. It is not an intermediary position.
Mastercard seeks a Performance Analytics consultant who leverages data and technology solutions to craft strategic insights for clients across various industries, integrating proprietary platforms with traditional management consulting. The role’s differentiator is the combination of Mastercard’s rich data assets with strategic advisory, though the risk of handling sensitive financial data cannot be ignored. Candidates must manage complex projects and client relationships, with no explicit salary details provided.
SCALIAN sells itself as a top French engineering consultant with CAC40 clients and a 5,500-strong multinational footprint; the data-visualization role reads more like a tooling specialization than a data platform revolution. You’ll be a BI/Tableau expert embedded in a cross‑functional team, shaping requirements, KPIs, and dashboards while auditing and tuning reports for performance. The stack leans hard on Tableau, with Power BI and Cognos, plus Python, SQL, and JavaScript, plus a Hadoop ecosystem (Hue, Hive, Zeppelin). Training, knowledge sharing, and Agile rituals are promised, and there’s work on proposals and knowledge transfer at mission end. Pros: strong brand, formal processes, formal onboarding, extensive toolset exposure. Cons: tool-centric, potential vendor lock-in, opaque salary details, and consulting cadence that can eat time. If you want a big-firm lab to sharpen Tableau chops, this is plausible; if you crave a tool-agnostic data engineering pace, look elsewhere.
The role requires supporting marketing functions with data and insight, using SQL, Python, and Spark, while working with big data in the Hadoop ecosystem. The job emphasizes problem-solving, attention to detail, and collaboration with cross-functional teams. While the skills align with data engineering, the focus on marketing analytics and the inclusion of VBA/Spark add a unique dimension. The role stands out for its practical focus on real-world data challenges, though the emphasis on marketing may limit its appeal to purely data engineering audiences. The salary is not explicitly provided, and the tools list is sparse but includes relevant technologies.
Morgan Stanley's AVP, Client Engagement Analytics sits at the crossroads of data engineering, business intelligence, and strategic influence within Wealth Management. It’s a hands-on role: tame disparate data, build end-to-end dashboards from sourcing to publishing, and deliver actionable insights that move the needle on client engagement and ROA. Expect to code in SQL and Python, visualize in Tableau, and work with Snowflake, Dataiku, Hue and Salesforce; establishing a Data Mart to deliver a single version of truth is central, with QA and risk controls baked in. Collaboration across Sales, Strategy, Product, Finance, and Technology is constant, and leadership must be persuaded with crisp storytelling. The pay is solid for the market, but bureaucracy and governance slow results; a genuine platform for impact, minus the buzzword bingo.
This role presents a challenge for a data engineer seeking to deliver meaningful value in a fast-paced, multi-platform environment. The position requires deep technical proficiency in SQL, Python, and big data technologies, with a focus on ETL pipeline design and maintenance. While the job description highlights strong analytical and communication skills, it lacks specific details about the company’s culture, team dynamics, or the extent of responsibility in a real-world setting. The role is technically demanding but lacks clarity on the company’s approach to innovation and collaboration. The opportunity lies in working with a large, dynamic organization that values technical excellence, but the description does not fully convey the nuances of the environment or the potential for growth within the team.
