IT Data Engineer

• Work directly with business users to gather requirements, clarify business logic and propose suitable data solutions.
• Design data models for reporting: define grain, metrics and dimensions, and implement them as maintainable dbt projects.
• Build and own streaming and batch pipelines from Kafka and source databases into the warehouse and data marts, using Airflow, dbt, and Postgres.
• Migrate data, business logic and reports from the legacy data warehouse to the new platform, including backfill, parallel run, reconciliation, cutover and rollback.
• Deliver new data features and products end to end, from design to deployment and handover.
• Deploy pipelines and services through GitLab CI following GitOps practices; keep all changes version-controlled and reviewed.
• Monitor pipelines and data quality daily (tests, freshness, reconciliation, alerts); troubleshoot incidents and fix root causes.
• Tune performance of SQL queries, dbt models and Airflow jobs.
• Join on-call rotation for critical data products.
• Maintain documentation: data models, lineage, runbooks, architecture diagrams.
• Mentor team members through code review, design review and pairing; set and maintain team standards for modeling, dbt and CI/CD.

• 5+ years in data engineering, including 3+ years running dbt and Airflow in production.
• Has designed and owned at least one data platform component or data product end to end.
• Has led or played a key role in at least one migration from a legacy data warehouse to a new platform.
• Experience working with on-premise or self-hosted data platforms (not only managed cloud services)

Technical expertise (must have)

• Data modeling (advanced): dimensional modeling (Kimball, star/snowflake schema, SCD types, fact table design, grain definition). Designs models that stay stable as business requirements change.
• Dbt Core (advanced): project structure (staging/intermediate/marts), incremental models, snapshots, macros and Jinja, packages, tests and documentation; orchestrates dbt runs with Airflow and sets up CI in GitLab (e.g. slim CI using manifest/state artifacts); optimizes model performance and run time
• GitOps & CI/CD (advanced): branching and merge request workflow, automated testing and environment promotion in GitLab CI; declarative deployments on Kubernetes (Helm, ArgoCD or equivalent).
• Data migration (advanced): assesses and re-implements legacy transformation logic, backfills historical data, runs old and new systems in parallel, reconciles results, and plans cutover and rollback.
• SQL & Postgres (advanced): writes complex analytical SQL (window functions, CTEs, incremental logic); reads query plans and tunes indexes, partitioning and queries for reporting workloads.
• Kafka (proficient): creates and configures topics; builds producers and consumers in production; understands partitioning, consumer groups, offsets and schema management; handles late, duplicate or out-of-order events.
• Python (proficient): writes production-quality code for pipelines and tooling (structured, tested, reusable); builds custom Airflow operators and hooks.
• Kubernetes (proficient): deploys and runs data workloads as pods: resource sizing, config and secrets, reading logs and troubleshooting failures. Cluster administration not required.

Soft skills

• Takes ownership of problems through to resolution, not just the assigned task.
• Communicates clearly with non-technical business users; able to challenge requirements and explain trade-offs.
• Willing and able to mentor less experienced engineers.
• English: communicates confidently in both speaking and writing with foreign colleagues, including technical discussions and documentation.

Nice to have

• Banking, fintech or payments domain; knowledge of SBV regulations.
• Spark.
• Grafana, Prometheus.
• Data governance: classification, masking, access audit.

Education

• Bachelor's degree in IT, Computer Science or related fields (preferred).