Senior Big Data Engineer (Python | AWS)

Responsibilities

Role Summary Own the data platform that powers clinician discovery, credential unification, and care-quality analytics. Design resilient, low-latency ingestion and transformation at scale (batch + streaming) with GDPR-by-design. Your work underpins search, matching, and ML features in our telemedicine platform across Germany.

Key Responsibilities

• Design and operate AWS-native data lakehouse: Amazon S3 + Lake Formation (governance), Glue/Athena (ELT), and optional Amazon Redshift for warehousing.
• Build high-throughput ingestion and CDC pipelines from partner APIs, files, and databases using EventBridge, SQS/SNS, Kinesis/MSK, AWS DMS, and Lambda/ECS Fargate.
• Implement idempotent upserts, deduplication, and delta detection; define source-of-truth governance and survivorship rules across authorities/insurers/partners.
• Model healthcare provider data (DDD) and normalize structured/semi-structured payloads (JSON/CSV/XML, FHIR/HL7 if present) into curated zones.
• Engineer vector-aware datasets for clinician/patient matching; operate pgvector on Amazon. Aurora PostgreSQL or use OpenSearch k-NN for hybrid search.
• Establish data quality (freshness, accuracy, coverage, cost-per-item) with automated checks. (e.g., Great Expectations/Deequ) and publish KPIs/dashboards.
• Harden security & privacy: IAM least-privilege, KMS encryption, Secrets Manager, VPC endpoints, audit logs, pseudonymised telemetry; enforce GDPR and right-to-erasure.
• Observability-first pipelines using OpenTelemetry (ADOT), CloudWatch, X-Ray; DLQ handling, replay tooling, resiliency/chaos tests; SLOs and runbooks.
• Performance tuning for Aurora PostgreSQL (incl. indexing, partitioning, vacuum/analyze) and cost-aware Spark (EMR/Glue) jobs.
• CI/CD for data (Terraform/CDK, GitHub Actions/CodeBuild/CodePipeline); test automation (pytest/DBT) and blue/green or canary for critical jobs. Desired Candidate Profile
• 6+ years in data engineering at scale; proven delivery in production systems (regulated domains a plus).
• Expertise in Python and SQL; hands-on with Spark (EMR/Glue) and stream processing (Kinesis/MSK/Flink/Spark Streaming).
• Deep AWS experience across S3, Glue, Athena, Redshift or Aurora PostgreSQL, Lake Formation, DMS, Lambda/ECS, Step Functions, EventBridge, SQS/SNS.
• PostgreSQL mastery incl. query planning, indexing, and performance tuning; familiarity with pgvector or OpenSearch vector search.
• Strong grasp of idempotency, deduplication, CDC, schema evolution, SCDs, and contract testing for data products.
• Observability (OpenTelemetry), CI/CD, and IaC (Terraform/CDK) best practices; strong incident response and on-call hygiene.
• Security-by-design mindset: data minimization, encryption, secrets, PII-safe logging; working knowledge of GDPR and auditability.
• Effective communicator across Product, Platform, Data Science, and Compliance; pragmatic,metrics-driven delivery.

Nice to Have

• Experience with FHIR/HL7, German TI/ePrescription/ePA integrations.
• DBT for transformations; OpenMetadata/Amundsen for catalog/lineage.
• Go for high-throughput services; experience with Bedrock or SageMaker for embedding generation.

How We Work & Benefits

• API-first, clean architecture, and pairing culture; mandatory code reviews.
• Remote-friendly with defined core hours; mission-led, patient-safety-first.
• Ownership mindset: you build it, you run it (with sensible SLOs and error budgets).

Compliance & Notes

• All PHI/PII processed within EU regions (e.g., eu-central-1); strict key management via AWS KMS and Secrets Manager.
• Right-to-erasure and lawful-basis handling embedded in data lifecycle (tombstones, purge workflows, and immutable audit trails). Back