Data Engineering Team Lead

💰 Salary: $29 300 - $33 000 per month

🏙️Hybrid: 3 days on-site/2 days remote (Office located in Warsaw)

🕦 Full-time position, long-term

☑️ Contract of Employment

Company is an enterprise software firm building AI-driven tooling for large organizations, operating across multiple international offices. Its platform helps clients in financial services, the public sector, and private industry unify data, automate workflows, and act on predictive signals.

Requirements:

• At least 7+ years working in data engineering, data architecture, or comparable technical positions

• 5+ years of experience of building production-grade data platforms on a major cloud provider (AWS, GCP, or Azure)

• 5+ years writing advanced SQL with RDBMSes

• 5+ years shipping ETL/ELT workflows using orchestrators such as Airflow or Prefect

• Solid command of DBT for building, validating, and documenting transformation layers

• 5+ years of hands-on work developing and deploying ETL/ELT pipelines using Airflow, Prefect, or similar orchestration tools

• Proven experience designing warehouses that handle both OLTP and OLAP patterns, with strong command of star schemas, snowflake schemas, facts, and dimensions

• Comfortable producing conceptual, logical, and physical models using established modeling tooling

• Worked extensively with at least one managed cloud warehouse (Snowflake, Redshift, BigQuery, or similar)

• Track record of putting validation, QA, and automated test coverage in place for data flows

• Working knowledge of how upstream data design enables ML use cases, including feature serving and similarity search workloads

• Solid grasp of governance practices, quality programs, and metadata tooling

• Demonstrated track record of leading, coaching, and growing technical staff

• Advanced spoken and written English 

Nice to Have:

• Python, particularly with Pandas and PySpark

• Containerization with Docker and orchestration via Kubernetes

• Automated build and deployment pipelines for data systems

• AWS, including Lambda and Step Functions

• Designing partitioning schemes for large datasets

• Production experience on Databricks

• Vector store implementations using Pinecone, Weaviate, or pgvector

• Familiarity with data mesh or data fabric design approaches

• Background in graph stores or knowledge graph modeling

• Cloud platform certifications

Key Responsibilities:

• Own the data architecture, standards, and reference designs supporting reporting, analytics, and ML workloads

• Define modeling rules across the org: star/snowflake schemas, flattened tables, OLTP vs OLAP, and AI-friendly structures

• Build cloud-native platforms on AWS (Redshift, RDS, Glue, Lake Formation) or comparable stacks, balancing performance, security, and cost

• Own the DBT transformation layer, keeping models modular, tested, and documented

• Orchestrate pipelines through Airflow and Prefect across scheduled ETL, streaming, event-driven, and API-based flows, with graceful failure handling

• Stand up validation, QA, and testing so pipeline outputs stay correct and consistent end to end

• Set data quality SLAs with monitoring, alerting, and automated reconciliation

• Run the governance program: quality, lineage, cataloging, classification, and access control

• Partner with engineering, product, and analytics to turn business needs into durable designs

• Own vendor and tooling decisions for the data stack in your domains

• Plan partitioning, indexing, and query tuning for high-volume, large-dataset workloads

• Define and maintain data contracts, schemas, and API specs across services and teams

• Shape datasets and pipelines to feed ML workflows, including feature stores, embeddings, and training data

• Run architecture and code reviews to keep patterns, performance, and maintainability on track

• Mentor engineers on modeling, cloud design patterns, and architecture decisions

• Mature release automation and CI/CD for data infrastructure and pipeline deployments