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# Lead Data Engineer (Partner Company Role)

- **Company:** FYST
- **Location:** Germany (Remote available)
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Airflow, Amazon Web Services, Amazon Elastic Compute Cloud, Amazon S3, Cloud Database, Information Engineering, Python (Programming Language), PostgreSQL, RabbitMQ, Snowflake, Debezium, Kubernetes, Apache Kafka, Data Delivery
- **Published:** June 30, 2026
- **Apply:** https://de.indeed.com/viewjob?jk=5a08a8bd8f6fb6e5

## About the Role

Do you have experience in gRPC?, * 5+ years in data engineering with end-to-end ownership of production pipelines.

 * Hands-on with Snowflake, PostgreSQL CDC (Debezium preferred), and Kafka.
 * Solid AWS experience - S3, RDS, Aurora, and cloud data infrastructure.
 * Data quality engineering mindset: monitoring, reconciliation, lineage.
 * Comfortable defining data contracts and driving requirements with backend engineering teams.
 * Technical leadership experience: project ownership, cross-team alignment, delivery under constraints.
 * Kubernetes, Airflow/Prefect/Dagster, and dbt are strong pluses.
 * Payments domain knowledge - settlement, transaction lifecycle, processor integrations - is a strong plus.
 * Familiarity with gRPC, RabbitMQ, and reading Go/Python service code is expected.
 * English language skills at the B2 level and fluent russian language.

## Description

* Implement CDC-based ingestion from PostgreSQL services (RDS, Aurora, EC2, K8s operator) using Debezium or equivalent.
 * Build streaming and near-real-time pipelines with defined SLAs.
 * Build a data quality control layer: checksums, reconciliation, schema validation, anomaly detection, and alerting.
 * Define quality checkpoints across the full pipeline - from source capture through Snowflake to client delivery.
 * Define and enforce data contracts with service-owning teams for core entities: transaction, merchant, settlement, and processing status.
 * Build the external data delivery layer: financial settlement, transaction status, processor reconciliation, and client analytics.
 * Design tenant separation and implement replay/reload mechanisms for failure recovery.
 * Start hands-on, then gradually hire and grow a small data engineering team as the function matures.
Build a pragmatic roadmap with concrete deliverables at 3, 6, and 12 months.


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