Back in the dark ages of Big Data – roughly 2010 – when Hadoop was the only game in town and SQL skills were suddenly in demand again, came Apache Hive. The promise? Let analysts query massive datasets using familiar SQL, translating it all into MapReduce jobs under the hood. Quite clever, really, though one does wonder if anyone actually enjoyed waiting several minutes for a simple count query to complete.
Hive essentially became the on-ramp for business intelligence folks to Hadoop, and for a while, it was indispensable. But then came Impala, Presto, Spark SQL… all offering drastically faster query performance. Hive still chugs along, largely as a data warehousing solution and batch processing engine, especially in organizations with deeply entrenched Hadoop ecosystems. It's a bit like that reliable, slightly rusty pickup truck in your garage – not glamorous, but it gets the job done… eventually.
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...
read more »
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.
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.
Master Power BI fast: build basic-to-advanced visuals, apply smart design, use dynamic slicers/filters & new cards, choose the right chart, spotlight key insights, brand and theme reports, and seamlessly publish/share in the Service.
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.
The role involves analyzing high-dimensional, real-time datasets to uncover fraud patterns, with a notable emphasis on data storytelling and visualization. The unique aspect is working with...
read more »
The role involves analyzing high-dimensional, real-time datasets to uncover fraud patterns, with a notable emphasis on data storytelling and visualization. The unique aspect is working with Walmart's expansive data universe, though the risk lies in the reliance on only SQL, Python or R, and big data tools, which may not fully address all analytical challenges.
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.
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.
The BigQuery Cost Optimization Guide is a valuable resource that offers a deep dive into understanding and managing the complexities of BigQuery costs. It provides practical guidelines and strategies to help you optimize both storage and compute expenses, ensuring you get the most value out of...
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...
read more »
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.
This position at Deloitte's Artificial Intelligence & Data team in Singapore is ideal for fresh graduates or recent entrants looking to build a technical foundation in data engineering amidst an...
read more »
This position at Deloitte's Artificial Intelligence & Data team in Singapore is ideal for fresh graduates or recent entrants looking to build a technical foundation in data engineering amidst an avalanche of corporate flummery. Candidates will wield SQL, Oracle, and an assortment of Big Data tools while navigating data integration complexities. The promise of working with industry giants might be enticing, but the reality of rampant buzzword usage—'cognitive technologies'—raises eyebrows. With a focus on emerging technologies, the role offers genuine opportunities for growth and learning, assuming you can survive the inevitable bureaucratic layers. The requirement for knowledge in AI, ML, and various development methodologies indicates a demanding expectations set, which, combined with a sprinkle of empty panegyrics, might leave you levelling up your coffee consumption just to stay afloat.
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.
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.
The role involves analyzing high-dimensional, real-time datasets to uncover fraud patterns, with a notable emphasis on data storytelling and visualization. The unique aspect is working with Walmart's expansive data universe, though the risk lies in the reliance on only SQL, Python or R, and big data tools, which may not fully address all analytical challenges.
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.
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.
This position at Deloitte's Artificial Intelligence & Data team in Singapore is ideal for fresh graduates or recent entrants looking to build a technical foundation in data engineering amidst an avalanche of corporate flummery. Candidates will wield SQL, Oracle, and an assortment of Big Data tools while navigating data integration complexities. The promise of working with industry giants might be enticing, but the reality of rampant buzzword usage—'cognitive technologies'—raises eyebrows. With a focus on emerging technologies, the role offers genuine opportunities for growth and learning, assuming you can survive the inevitable bureaucratic layers. The requirement for knowledge in AI, ML, and various development methodologies indicates a demanding expectations set, which, combined with a sprinkle of empty panegyrics, might leave you levelling up your coffee consumption just to stay afloat.
