Senior Data Platform Engineer
Role details
Job location
Tech stack
Job description
Taxfix makes tax filing simple, fast, and accessible for millions of users across Europe. Behind the product sits a data platform that powers everything from business analytics and regulatory reporting to ML and AI-driven product features. We're growing and the platform needs to grow with us., We are looking for an experienced Data Platform Engineer to design, build, and operate the infrastructure and pipelines that make data at Taxfix reliable, compliant, and ready for AI. You will own the systems that move data from operational databases, APIs, and SaaS tools into our analytical environment and make sure that data is correct, timely, and safe to use. Your work will directly enable ML and AI-powered product experiences, self-serve analytics, and regulatory reporting., * Contribute to and operate cloud platform infrastructure - manage GCP resources (GCS, Dataflow, Dataproc, k8s, Pub/Sub…), provision and maintain environments with Terraform and help ensure the platform is reliable, predictable, cost-efficient and scalable
- Build and maintain ingestion pipelines that capture changes from application databases, APIs, SaaS and deliver clean, analytics-ready tables to our cloud data warehouse
- Operate and improve our orchestration layer - scheduling, retries, SLA tracking, and observability for data pipelines
- Design data models with proper layering that handle real-world data complexity: out-of-order events, schema evolution, late arrivals, and backfills
- Own data quality monitoring - build validation, monitoring, and alerting that catches problems before downstream consumers do
- Implement privacy and compliance controls - anonymization, pseudonymization, access policies, and deletion propagation (GDPR right-to-be-forgotten) across raw and derived layers
- Prepare data for ML and AI use cases - build governed, privacy-safe datasets and feature pipelines that ML engineers and data scientists can use for model training, evaluation, and production inference
- Use AI effectively in engineering workflows - leverage AI-assisted development where it improves delivery quality and speed, and share best practices with the team
- Build scalable AI platform capabilities - develop reusable, production-grade patterns for AI workloads (governed data access, privacy-safe telemetry, evaluation datasets, and cost/quality monitoring) that multiple teams can adopt, * A chance to do meaningful, people-centric work with an international team of passionate professionals.
- Holistic well-being with free mental health coaching sessions and yoga.
- A monthly allowance to spend on an extensive range of services that you can use and roll over as flexibly as you like.
- Employee stock options for all employees because everyone deserves to benefit from the success they help to create.
- 30 annual vacation days and flexible working hours.
- Work from abroad for up to six weeks every year. Just align with your team, and then enjoy your trip.
- Plenty of opportunities to socialise as a team. In addition to internal tech meetups, our international team hosts regular get-togethers - virtually and in person when possible.
- Free tax declaration filing, of course, through the Taxfix app - and internal support for all personal tax-related questions.
- Have a four-legged friend in your life? We're happy to have dogs join us in the office.
Requirements
Do you have experience in Terraform?, * 4+ years of experience in Data Engineering or a similar role (backend engineer working on data-intensive systems counts)
- Strong Python skills for data pipeline development - you write production code, not just scripts
- Strong SQL skills - window functions, CTEs, query optimization are second nature
- Experience with event-driven data pipelines - CQRS, event ordering, idempotency, and the difference between initial load and incremental processing
- Expert with Airflow - you've built DAGs with proper task dependencies, retries, and monitoring
- Strong Snowflake or other modern data warehouse knowledge - resource management (compute/warehouse sizing, concurrency), security & governance (roles/RBAC, column/row-level masking), columnar storage & partitioning/clustering concepts, and query performance & cost optimization in production
- Cloud platform experience - you've worked with GCP (GCS, Dataflow, Dataproc etc) or equivalent AWS/Azure services and understand how to manage cloud resources at scale
- Infrastructure-as-code - experience with Terraform, Helm, or similar tools for provisioning and managing cloud environments
- K8S and Docker containerization - you package and deploy your own work
- Data quality mindset - you profile data, validate assumptions, build checks, and don't trust that "the data looked clean"
- Data for AI readiness - you have experience preparing data for ML and AI use cases with appropriate governance, lineage, and privacy controls
- Awareness of data privacy requirements - you can identify PII, understand GDPR, and know how to implement anonymization and deletion across multiple data layers
- AI-enabled engineering practices - you actively use AI assistants and code generation tools to accelerate development and deliver, and you share effective practices with the team
Benefits & conditions
Pulled from the full job description
- Flexible schedule