Senior Data Engineer
Kraków - Poland, Polska
Key offer highlights
Data: SQL / BI / Python
DevOps / Cloud: AWS, Azure, Docker, Kubernetes
Looking for experts - senior/expert
Full-time
Description
Compensation details will be shared by the recruiter during the first interview.
How we work
Lead and Inspire: Drives trust, alignment, and enthusiasm
Think Big: Focus on the problems that most impact commercial outcomes
Champion the client: Understand and prioritise client's needs
Deliver at pace: Push for fast, sustainable growth;
What you’ll do
Design and build robust batch and real-time data pipelines on GCP, including Kafka-based event streams, BigQuery transformations, and Airflow-orchestrated workflows, as part of the GCP consolidation and Medallion architecture build-out
Own data quality within your delivery area: define and implement data contracts, quality checks, and observability instrumentation so that pipeline health is visible and SLAs are met
Develop and maintain in-house connectors and third-party native integrations that form part of IG’s ingestion estate, ensuring resilience, monitoring, and long-term maintainability
Contribute to engineering standards and best practices across the squad — including code review, dbt modelling patterns, CI/CD pipeline hygiene, and change control for shared datasets
Support feature engineering and model data pipelines for Data Science, and help lay the foundations for AI-ready data — including lineage, reproducibility, and freshness guarantees for ML workloads
What you’ll need for this role
Proven hands-on experience building and operating production-grade data pipelines on GCP — including BigQuery, Cloud Composer/Airflow, GCS, and Cloud Run — with strong SQL and Python skills
Solid experience with Apache Kafka or equivalent streaming technologies, including designing event-driven ingestion patterns and troubleshooting real-time data flows under operational conditions
Familiarity with modern transformation tooling, particularly dbt, and an appreciation of Medallion (Bronze/Silver/Gold) architecture patterns and how they support self-service analytics
A quality-first mindset: experience implementing data contracts, lineage tracking, observability tooling, and pipeline SLO monitoring in a production environment
Comfortable working in a regulated financial services environment, with an understanding of data governance requirements (lineage, access control, audit trails) and the discipline to operate within change control processes
Raise the bar: Take ownership, be accountable and share feedback