Top Data Analyst Jobs in Warsaw

Investigate and improve fraud signals, user segmentation, and review processes for a player rewards platform. Analyze suspicious activity, monitor segment performance, evaluate verification methods across markets, investigate manually flagged users, and recommend third-party fraud prevention tools. Collaborate with product, data, and gameplay analysts to translate findings into operational fraud controls and revenue-share decisions.

Analyze and transform R&D master data using Python, PySpark, and SQL; identify data-quality issues; and build Power BI dashboards with advanced DAX models. Support AI/ML initiatives through feature engineering, training datasets, and predictive algorithms. Translate stakeholder needs into actionable BI features while ensuring compliance with data governance and GxP documentation standards.

Analyze and improve fraud signals and user segmentation for Puzzleplay’s rewards platform. Investigate suspicious and manually reviewed users, monitor segment effectiveness, identify gaps in detection, and recommend verification tools. Collaborate with product and data teams to turn analysis into operational fraud controls across different markets as fraud patterns evolve.

Enhance environmental product databases, develop life cycle assessment models, create data-on-demand datasets, support client projects and customer inquiries, and evaluate JIRA issues. The role involves coordinating sector upgrades, managing database content through Scrum processes, applying SQL, Python, and AI tools, and developing expertise in life cycle assessment, sustainability, and Sphera software.

Develop and maintain Power BI dashboards and operational risk reports, prepare Risk Committee materials, analyze incidents and emerging risk patterns, monitor KRIs and KCIs, and improve risk data quality and governance. Support eFront UAT and business operations, maintain risk appetite reporting, and contribute to analytics transformation initiatives involving AI and advanced analytics. Collaborate with international risk managers and stakeholders across the organization.

Perform daily investment data controls, reconciliations, data-quality reviews, and audit support using Clearwater and Excel. Collaborate with internal and external stakeholders, database providers, and auditors while supporting finance and data projects. The role requires strong analytical skills, attention to detail, investment interest, and availability three to four days per week during a six-month paid internship.

Supports IRC Poland’s MEAL and Programmes teams by integrating CommCare, KoboToolbox, and other data systems through OpenFn, APIs, and ETL processes. Builds and maintains CommCare-to-Excel connections, dashboards, datasets, and recurring reports. Monitors data flows, troubleshoots synchronization issues, documents integrations, and translates complex data into clear findings for technical and non-technical stakeholders.

Own regulatory analytics and reporting by building audit-ready datasets, pipelines, dashboards, compliance metrics, and automated workflows. Analyze complex data, monitor quality, support audits and regulatory exams, and deliver actionable insights to compliance teams and leadership. Translate regulatory requirements into accurate reports using Python, SQL, dbt, and Airflow while partnering cross-functionally to manage data issues and remediation.

Analyze large datasets covering customer behavior, marketing campaigns, and business performance. Build analytical datasets with SQL and PySpark, transform multi-source data, ensure data quality, and support segmentation, funnel, retention, churn, ROI, and campaign analysis. Collaborate with marketing analysts, stakeholders, and data teams to resolve data issues, improve workflows, optimize data models, and deliver ad hoc insights.

Analyze paid and organic marketing performance, allocate spend, build acquisition and conversion dashboards, investigate funnel and data issues, monitor attribution, and evaluate A/B experiments. The role provides data-driven recommendations to a cross-functional Demand and Supply team and requires strong SQL, Python, statistics, BI, communication, and marketing analytics skills.

The Data Analyst will build and maintain dbt models, analyze billing and SaaS metrics, monitor data quality, maintain Metabase reporting, and support acquisition due diligence. The role involves tracing data from source systems through ingestion and transformation, resolving production issues, validating results, and communicating findings to technical and business stakeholders. The analyst will also maintain Airbyte integrations, document data definitions, and use AI tools responsibly in daily data work.

Analyze product and business metrics, user behavior, retention, conversion, revenue, and lifecycle performance. Use SQL and BI tools to prepare data, build dashboards, investigate anomalies, monitor data quality, and identify root causes. Provide ad hoc analyses and actionable recommendations to product and leadership teams while documenting metrics and findings.

Build propensity and machine learning models to predict customer behavior, improve campaign targeting, and evaluate campaign performance. Develop automated dashboards and reporting tools, analyze business data, present insights to senior stakeholders, and support client engagements, proposals, and consulting solution development.

Coordinate the functional delivery of a Databricks HR data product that combines Mirakl WiFi, Zoom room booking, and Workday data. Align business, HR, data ownership, privacy, architecture, and engineering stakeholders to define scope, ensure reliability, document the solution, and maintain compliance with data protection requirements.

Owns functional and delivery coordination for data platform initiatives, translating business needs into requirements, user stories, and acceptance criteria. Coordinates stakeholders and engineering teams through discovery, development, validation, acceptance, and handover. Investigates data structures, sources, transformations, business rules, and quality issues using SQL or equivalent tools. Maintains documentation in Confluence and supports reporting and analytics teams with clear data definitions and context.

Own GTM and sales analytics across pipeline performance, forecasting, funnel conversion, win/loss analysis, lead and account scoring, territory planning, quota modeling, and capacity planning. Partner with RevOps, Sales, Product, Finance, and Analytics Engineering to improve metric definitions, Salesforce and product data models, dashboards, and decision-making. Translate complex analysis into clear recommendations for GTM leadership.

Supports data product owners through backlog refinement, requirements gathering, pipeline change testing, data quality, cataloging, lineage, and governance activities. Coordinates Data Engineering, Architecture, Quality, Catalog, and Operations teams to develop and deploy governed data assets. Facilitates Agile ceremonies, manages business metrics lineage, and supports analytics and reporting products across business and technology stakeholders.

Lead enterprise Customer 360 and Graph Intelligence business analysis across customer, account, asset, product, contract, finance, service, and interaction data. Gather and document business, functional, and data requirements; facilitate stakeholder workshops; define use cases, KPIs, workflows, user stories, and acceptance criteria. Support prioritization, MVP and roadmap planning, solution delivery, UAT, rollout, adoption, and business-case development while bridging business and technical teams.

Maintains customer account, security, pricing, and reference data across multiple systems. Identifies data-quality and client-service improvements, supports risk and compliance controls, backs up the manager, guides team members, evaluates performance, and helps manage project initiatives and junior staff delivery.

Conducts data operations for client databases and reports by validating incoming data, coding products and characteristics, managing samples, monitoring file flows, and identifying data discrepancies. Maintains product catalogues, process documentation, statistical samples, and expansion factors. Investigates data inquiries, determines root causes, coordinates resolutions with local teams, and ensures product data accurately reflects market conditions.

Analyze website traffic, conversion funnels, and digital campaign performance. Build automated dashboards in Looker Studio or Tableau. Write complex SQL to extract and blend data, manage GA4 tracking and BigQuery exports, generate insights tying web data to business outcomes, and support A/B test analysis for CRO.

Oversee the creation and maintenance of customer account, security, and pricing data across multiple systems. Identify data quality and client service improvements, support compliance and risk controls, provide team guidance, back up the manager, and supervise junior staff delivering project initiatives. Analyze inconsistencies, recommend policy and process improvements, assess risks, and ensure adherence to applicable regulations and company policies.

The Senior Data Analyst will provide product, operations, and business analytics; analyze internal, market, and customer data; translate findings into actionable insights; improve product features; define data models; ensure data quality; and build dashboards and analytical models using Snowflake, Looker, Amplitude, and related tools. The role also involves A/B testing, causal inference, stakeholder communication, and collaboration with data engineers.

Ensures accurate, reliable, and timely client data delivery by cleaning, transforming, validating, and monitoring workflows. Investigates data inconsistencies, maintains data integrity standards, documents processes, and collaborates with cross-functional teams to meet operational and client needs. The role also supports process improvements, efficiency, scalability, and effective management of multiple workflows and deadlines.

Ensures accurate, reliable, and timely client data delivery by cleaning, transforming, validating, and monitoring data workflows. Investigates data inconsistencies, maintains data integrity standards, documents processes, collaborates with cross-functional teams, and contributes to process improvements that increase operational efficiency and scalability.