Data Engineer – Cloud Pipelines (Databricks & AWS)

About the role

The Data Engineer builds and operates the data pipelines of the cloud data platform, from ingestion to the curated layers that serve analytics, BI, ML and AI. The role sits in the Data Tribe, reports to the Data Engineering Team Lead and works on a modern lakehouse built on Databricks and AWS, serving multiple brands and markets in a regulated industry. Alongside delivery, the engineer mentors junior engineers and helps the team adopt the platform's standards.

Key responsibilities

• Design, build and maintain batch and streaming pipelines (Kafka, Spark Structured Streaming, batch jobs) on Databricks and AWS, implementing the medallion layers (Bronze/Silver/Gold)
• Develop in Python/PySpark and SQL, following the team's coding, testing and deployment standards (version control, CI/CD, Databricks Asset Bundles)
• Build CDC and event-based ingestion from relational and streaming sources, including schema evolution
• Participate in migrating existing workloads to the new platform, from a cloud data warehouse and on-premises sources, within the brand/market isolation and data sharing model
• Contribute to operational readiness: monitoring, alerting, runbooks and on-call support for pipelines in production
• Implement data quality checks, reconciliation and data contracts, and maintain lineage, documentation and metadata
• Apply security and compliance requirements: access control, data classification, retention and auditability
• Mentor junior engineers through code reviews, pairing and onboarding, and work with Data Admins, DevOps, BI and ML teams

Required profile

• 3+ years of hands-on data engineering experience building production pipelines
• Experience with streaming data (Kafka or Amazon MSK and Spark Structured Streaming, including CDC patterns)
• Experience with at least one major cloud, preferably AWS (S3, Glue, MSK, Redshift, Lambda, IAM)
• Experience with data modelling for analytics and the medallion/lakehouse approach
• Experience with version control, CI/CD and automated testing for data pipelines
• Good knowledge of relational databases (PostgreSQL, SQL Server) and data warehouse concepts
• Ability to translate business requirements into documented pipelines and to mentor less experienced engineers
• Good written and spoken English

Required skills

• Strong Python and SQL; distributed data processing and performance tuning
• Apache Spark (PySpark) and SQL in production; Databricks (Delta Lake, Workflows/DLT, Unity Catalog) is a strong advantage
• Preferable: Databricks or AWS certifications, Apache Iceberg, Terraform, Airflow, dbt
• Plus: experience migrating workloads to a cloud lakehouse, and regulated or high-volume domains such as gambling, fintech or e-commerce

What we offer

• Generous annual leave and hybrid work
• Individual health insurance and paid sick leave
• Paid maternity and paternity leave
• Family reward, performance and referral bonuses