Senior Data Platform Engineer

Role overview

As a Senior Data Platform Engineer at Taxfix, you will design, build, and operate the data platform infrastructure and pipelines that keep data reliable, compliant, and ready for AI. You’ll collaborate with cross-functional teams and work on systems that power data movement, processing, and governance.

Key missions

• Design, build, and operate infrastructure and pipelines to ensure data is reliable, compliant, and AI-ready.
• Own systems that move data from operational databases, APIs, and SaaS tools into the analytics environment.
• Contribute to the operation of the cloud data platform, managing GCP resources such as GCS, Dataflow, Dataproc, Kubernetes (k8s), and Pub/Sub.

Responsibilities

• Build production data pipelines (not just scripts) with operational quality.
• Support event-driven processing and ensure correctness via patterns like CQRS, event ordering, idempotency, and clear separation of initial load vs incremental processing.
• Implement and maintain data governance and privacy (PII handling, GDPR awareness, anonymization, and deletion across layers).
• Improve platform reliability and performance through monitoring, retries, and robust task dependencies.

Requirements

• Strong Python skills for data pipeline development; production-grade code.
• Kubernetes and Docker containerization; package and deploy your own work.
• 4+ years in Data Engineering or a similar role (backend engineer on data-intensive systems counts).
• Event-driven data pipelines experience.
• Knowledge of CQRS, event ordering, idempotency, and initial vs incremental processing.
• Infrastructure-as-Code experience (e.g., Terraform, Helm, or similar).
• AI readiness experience: preparing data for ML/AI with governance, lineage, and privacy controls.
• Strong Snowflake (or comparable modern data warehouse) knowledge.
• SQL expertise (CTEs, window functions, query optimization).
• Expert with Airflow: DAGs with task dependencies, retries, and monitoring.
• GCP experience (GCS, Dataflow, Dataproc, etc.) or equivalent AWS/Azure services.
• Data quality mindset: profiling, validations, checks, and not assuming data is clean.
• Awareness of privacy requirements including identifying PII and implementing anonymization and deletion.

Nice-to-haves

• Experience with security/governance mechanisms such as roles/RBAC and column/row-level masking.
• Resource management and cost optimization: compute/warehouse sizing, concurrency, and performance in production.