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...
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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.
NEXTON positions a confirmed Data Analyst in Paris on a CDI for a large client, blending consulting, agency, and ESN vibes. The remit centers on data collection from databases, internal systems,...
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NEXTON positions a confirmed Data Analyst in Paris on a CDI for a large client, blending consulting, agency, and ESN vibes. The remit centers on data collection from databases, internal systems, and external APIs, followed by statistical analyses and interactive dashboards via Tableau, Power BI, and Piano, with BigQuery as a data source. Governance and data quality are explicit, and the role leans Lean and Agile with close ties to decision-makers. The stack is heavy on BI tooling; no explicit scripting language or data engineering chores beyond data sourcing and cleaning. The job suits a mid-career analyst focused on e-commerce outcomes, reporting, and standardisation rather than ML or pipeline building. Opportunities lie in stakeholder influence, cross-client exposure, and governance discipline; risks include vendor lock-in of BI tools and a potentially narrow data-ops scope. Salary not disclosed; job seems solid but not groundbreaking.
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.
Intern - Quality Assurance Automation Engineer - F/M
@ sap
FR | 2025-12-26
SAP’s 6-month Paris internship asks a QA Automation Engineer to dive into SAP Analytics Cloud, collaborate in Scrum squads, and shape end-to-end test strategies for UI and API layers using Jasmine...
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Intern - Quality Assurance Automation Engineer - F/M
sap
(FR) INTERNSHIP | JOB LISTED
SAP’s 6-month Paris internship asks a QA Automation Engineer to dive into SAP Analytics Cloud, collaborate in Scrum squads, and shape end-to-end test strategies for UI and API layers using Jasmine and Selenium. It’s heavy on toolchain: Gherkin, Cucumber/Robot, Java, JavaScript/TypeScript (UI5/React), Git, Jenkins, Docker, Kubernetes, plus an ambitious nod to an LLM-based test generator. The mix of front-end components and cloud analytics aligns with real-world demand, but the permanent perks are undefined in a six-month stint and the bilingual English/French requirement tightens the gate. The role promises constant learning and a window into large-scale enterprise CI/CD, with opportunities to influence QA approaches across global SAP teams. The risks? Bureaucracy, a vendor-light internship label, and a credential boost with a cloud behemoth that isn’t exactly a startup. Overall, practical, not flashy, valuable if you want visibility into enterprise analytics tooling.
Six-month internship at SAP Paris as QA and Automation Engineer. You join Scrum teams to master SAP Analytics Cloud, align with delivery workflows, analyze functional needs, and design/execute...
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Intern - Ingénieur.e Assurance Qualité & Automatisation - F/H
sap
(FR) INTERNSHIP | JOB LISTED
Six-month internship at SAP Paris as QA and Automation Engineer. You join Scrum teams to master SAP Analytics Cloud, align with delivery workflows, analyze functional needs, and design/execute automated UI and API tests with Jasmine and Selenium. You will track defects, validate fixes, and help stitch CI/CD pipelines; expect to contribute to test generation and LLM-based error analysis. The tech stack reads like a checklist: Java, JavaScript/TypeScript (UI5 and React), Selenium, Jasmine, Cucumber or Robot, Git/GitHub, Jenkins, Docker, Kubernetes, plus Gherkin knowledge a plus and basic AI/ML concepts. It sits inside SAP’s cloud analytics ecosystem (SAP Analytics Cloud, SAP BTP) with teams across Paris and the world. The role promises learning and well-being, but as an internship it tests you more than it rewards, and the breadth of tooling can be overwhelming for a six-month window.
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.
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.
NEXTON positions a confirmed Data Analyst in Paris on a CDI for a large client, blending consulting, agency, and ESN vibes. The remit centers on data collection from databases, internal systems, and external APIs, followed by statistical analyses and interactive dashboards via Tableau, Power BI, and Piano, with BigQuery as a data source. Governance and data quality are explicit, and the role leans Lean and Agile with close ties to decision-makers. The stack is heavy on BI tooling; no explicit scripting language or data engineering chores beyond data sourcing and cleaning. The job suits a mid-career analyst focused on e-commerce outcomes, reporting, and standardisation rather than ML or pipeline building. Opportunities lie in stakeholder influence, cross-client exposure, and governance discipline; risks include vendor lock-in of BI tools and a potentially narrow data-ops scope. Salary not disclosed; job seems solid but not groundbreaking.
Intern - Quality Assurance Automation Engineer - F/M
sap
(FR) INTERNSHIP | JOB LISTED
SAP’s 6-month Paris internship asks a QA Automation Engineer to dive into SAP Analytics Cloud, collaborate in Scrum squads, and shape end-to-end test strategies for UI and API layers using Jasmine and Selenium. It’s heavy on toolchain: Gherkin, Cucumber/Robot, Java, JavaScript/TypeScript (UI5/React), Git, Jenkins, Docker, Kubernetes, plus an ambitious nod to an LLM-based test generator. The mix of front-end components and cloud analytics aligns with real-world demand, but the permanent perks are undefined in a six-month stint and the bilingual English/French requirement tightens the gate. The role promises constant learning and a window into large-scale enterprise CI/CD, with opportunities to influence QA approaches across global SAP teams. The risks? Bureaucracy, a vendor-light internship label, and a credential boost with a cloud behemoth that isn’t exactly a startup. Overall, practical, not flashy, valuable if you want visibility into enterprise analytics tooling.
Intern - Ingénieur.e Assurance Qualité & Automatisation - F/H
sap
(FR) INTERNSHIP | JOB LISTED
Six-month internship at SAP Paris as QA and Automation Engineer. You join Scrum teams to master SAP Analytics Cloud, align with delivery workflows, analyze functional needs, and design/execute automated UI and API tests with Jasmine and Selenium. You will track defects, validate fixes, and help stitch CI/CD pipelines; expect to contribute to test generation and LLM-based error analysis. The tech stack reads like a checklist: Java, JavaScript/TypeScript (UI5 and React), Selenium, Jasmine, Cucumber or Robot, Git/GitHub, Jenkins, Docker, Kubernetes, plus Gherkin knowledge a plus and basic AI/ML concepts. It sits inside SAP’s cloud analytics ecosystem (SAP Analytics Cloud, SAP BTP) with teams across Paris and the world. The role promises learning and well-being, but as an internship it tests you more than it rewards, and the breadth of tooling can be overwhelming for a six-month window.
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.
