Top Data Engineer Jobs in Warsaw

Designs and builds production-grade AI applications natively on Snowflake using Cortex Analyst, Cortex Search, Snowflake Agents, semantic models, Python, and SQL. Develops secure, performant natural-language querying, retrieval, text-to-SQL, and agentic workflows over governed data. Owns delivery of a technical workstream, collaborates with data scientists, engineers, clients, and stakeholders, and supports enterprise GenAI and analytics solutions. Snowflake implementation experience is mandatory; regulated-industry experience is advantageous.

Build and operate production data pipelines supporting analytics, AI, and agentic workflows. Responsibilities include implementing canonical data models, maintaining Databricks or Snowflake platforms, monitoring reliability, responding to incidents, validating healthcare data mappings, improving performance and cost, and documenting architecture. Requires strong SQL and Python skills, cloud data platform experience, ETL/ELT orchestration expertise, and healthcare or pharmaceutical data experience.

Designs, builds, maintains, and optimizes enterprise data platforms, pipelines, warehouses, models, and integration frameworks supporting Power BI, analytics, AI, and operational reporting. The role develops Azure-based ETL/ELT solutions, improves data quality and reliability, establishes governance and standardized metrics, and collaborates with BI, IT, automation, and AI teams. It is a hybrid position in Katowice, Poland.

Design, implement, and maintain scalable streaming and batch data pipelines using Apache Pinot, Iceberg, Flink, and Spark. Improve pipeline performance, reliability, observability, and data quality while participating in software development, testing, incident resolution, and postmortems. Collaborate with engineering, product, and data teams, use AI coding tools, and continuously develop expertise in production data systems.

Develop and maintain production applications and data pipelines to support early drug discovery. Build APIs, backend services, and UIs; implement ETL workflows, data validation, and database integrations; collaborate with scientists and senior engineers; participate in code reviews, CI/CD, monitoring, and incident response to ensure reliable data-driven research systems.

Design and develop Azure-based data platforms, ETL/ELT processes, data warehouses, data marts, and data lakes. Write advanced SQL and Python, create Power BI visualizations, model data, and work with Databricks and Spark. Deploy data applications using Docker, Kubernetes, and GitHub while contributing to scalable client projects and maintaining data quality, monitoring, governance, and operational readiness.

Build the companys first data stack and data products (internal analytics and customer-facing). Work across data engineering, devops, and software tasks; ship early features, improve developer experience, document proposals, and iterate based on feedback.

Build and maintain a centralized data platform, including SQL Server warehouses, SSIS processes, scalable SQL/Python/Spark pipelines, data integrations, CI/CD pipelines, and Infrastructure as Code. The role also involves improving legacy code, ensuring data quality and reliability, contributing to architecture and engineering standards, conducting code reviews, documenting solutions, and collaborating with team members.

Designs, develops, tests, optimizes, and maintains scalable batch and near-real-time data pipelines and architectures. Ensures data quality, builds API integrations, improves internal data processes, and supports machine learning, data science, BI, and product initiatives. The role requires experience with data warehouses, relational and NoSQL databases, data modeling, event-driven architectures, SQL, Python, Airflow, Spark, and AWS data services.

Design and deliver cloud-native data platforms, scalable big data pipelines, lakehouses, warehouses, transformation frameworks, BI solutions, and ML-enabled data workflows. Lead client-facing pre-sales engagements, technical workshops, proof-of-concepts, executive conversations, and enterprise data modernization initiatives. Build reusable architectural IP, influence senior stakeholders, and translate ambiguous business challenges into production-ready solutions using AWS and modern data technologies.

Develop and maintain scalable AWS data engineering platforms and reusable Python modules. Build object-oriented data processing solutions, debug pipelines across Step Functions, Glue, Athena, and Lambda, and apply modern engineering practices including testing, CI/CD, and version control. Collaborate with agile teams to deliver reliable solutions across countries and business environments.

Design and implement Azure-based data processing systems, scalable data pipelines, data warehouses, data lakes, and ETL workflows. Transform and optimize structured and unstructured data, tune queries, improve storage and retrieval performance, and resolve system bottlenecks. Collaborate with data scientists, analysts, and stakeholders to deliver technical solutions while working within Agile methodologies.

Owns the design, development, operation, and governance of data pipelines and integration patterns supporting Medical Affairs AI products. Builds APIs and ETL/ELT solutions connecting enterprise systems to analytics platforms, RAG pipelines, vector databases, and GraphRAG applications. Ensures data quality, privacy, lineage, compliance, and protection of sensitive information. Partners with architecture, engineering, product, and business stakeholders to deliver reusable, production-grade data integrations.

Designs, builds, and supports GCP data ecosystems, including cloud storage, processing, warehouses, data lakes, pipelines, and integrations. Responsibilities include requirements gathering, data modeling, performance optimization, troubleshooting, monitoring, root cause analysis, and client collaboration. The role also mentors junior data engineers and contributes to ETL/ELT, orchestration, transformation, and database management initiatives.

Build and maintain AWS-based data pipelines and lifecycles (Glue, DMS, Redshift, S3). Develop production data transformations and CDC using Scala and Python, automate CI/CD with Bash, optimize Spark jobs (partitioning, Parquet, broadcast joins), and deliver reliable, idempotent pipelines while collaborating remotely with cross-functional teams.

Designs, builds, and operates production-grade agentic AI systems and orchestration frameworks. Responsibilities include prompt architecture, tool and API integrations, monitoring, evaluation, cost controls, reliability improvements, and governance documentation. The engineer collaborates with data science, data engineering, and governance teams to ensure reliable, compliant workflows using structured healthcare and pharmaceutical data.

Design, build, and maintain AWS-based lakehouse solutions and medallion architecture using Iceberg and Glue. Implement CDC and streaming ingestion with Debezium and Kafka. Develop PySpark batch/distributed jobs on EMR/Glue, orchestrate workflows with Airflow, ensure data quality/governance, and collaborate on infrastructure and IaC practices to deliver reliable business-ready datasets.

Build unstructured-data ingestion and context-layer pipelines for an investment group. Responsibilities include historical backfills, document parsing, entity resolution, deduplication, incremental API synchronization, embeddings, vector and graph storage, access scoping, lineage, PII protection, orchestration, monitoring, cost control, and operational runbooks.

Own the data engineering side of DoiT’s integrations framework by building and maintaining third-party billing and usage API integrations. Ensure data correctness, completeness, normalization, reconciliation, and support for negotiated rates. Manage orchestration, scheduling, backfills, and pipeline operations while using AI throughout development. Collaborate with product, support, and engineering teams to expand vendor coverage and improve cloud and SaaS spend visibility.

Own data engineering for a cloud and SaaS billing integrations platform. Build and maintain third-party billing API integrations, operate ingestion pipelines, normalize vendor data, manage orchestration and backfills, and ensure financial-grade correctness through reconciliation and quality checks. Use AI throughout the engineering workflow and collaborate with product, support, and platform teams to expand vendor coverage and improve customer cost visibility.

Build and maintain Python ETL pipelines, data warehouses, and data lakes supporting marketing analytics and personalized marketing programs. Integrate mobile marketing data, automate scalable and cost-efficient workflows, and ensure data quality, validation, governance, and performance. Collaborate with marketing and studio teams to deliver data-driven solutions. The role uses Snowflake or Firebolt, Airflow, SQL, distributed computing, and modern AI development tools.

Own the architecture, ingestion, governance, optimization, retention, and correctness of a large-scale financial data lakehouse. Design bronze, silver, and gold layers; manage CDC streaming through Kafka and Debezium; optimize TB-scale queries; ensure reconciliation, freshness, regulatory retention, GDPR deletion, lineage, access controls, PII masking, and infrastructure cost monitoring.

Own the company data lakehouse supporting millions of financial events daily. Design bronze, silver, and gold architectures; build CDC streaming ingestion; optimize large-scale storage and queries; manage retention, archival, and GDPR deletion; ensure reconciliation, freshness, and data quality; and implement governance, access controls, PII masking, encryption, lineage, and auditability. Monitor pipeline health, anomalies, and cloud costs across S3 and Snowflake or Databricks.

Design and develop an AI-powered analytics agent in Devin, integrating GitHub, dbt, Databricks, Snowflake, and data ingestion systems. Adapt dbt workloads for Databricks, resolve platform compatibility issues, build and test agent capabilities, and implement guardrails, validation, observability, security, and reliable AI-assisted workflows. Collaborate with client engineering teams, troubleshoot integrations, document the solution, and prepare knowledge-transfer materials.

Perform manual functional, regression, integration, exploratory, and end-to-end testing across web applications, backend systems, REST APIs, databases, and integrations. Investigate defects using browser DevTools, network tools, proxies, logs, API tools, and SQL. Support release readiness, validate fixes, communicate risks, and improve QA processes. Collaborate cross-functionally while adapting testing approaches across products and technical environments.