Staff Data Engineer, Taxfix

• 6+ 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
• Experience with Snowflake or/and BigQuery — you understand their architecture, performance characteristics, and how they differ from each other and from other analytical or operational tools
• 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 understand what it takes to prepare data for ML and AI: governance, lineage, privacy controls, and reproducibility
• 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 can establish standards for their effective use across the team
• Not sure if you meet all the requirements for this role? Please apply anyway. You might bring something special to the team that we hadn't considered previously

Desirable

• 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
• This is a hands-on engineering role. You will write code, review designs, and operate production systems not just draw diagrams
• The Data Platform team is part of the Data Organization, which enables everyone at Taxfix to make data-driven decisions, drive business growth, and power ML and automation in our products
• Within our group we have product analysts, analytics engineers, machine learning engineers, and data engineers working across the whole company
• The platform team serves many consumers analytics, ML, product, and compliance so you can expect to join cross-functional project teams regularly, working alongside backend and frontend engineers, analysts, ML engineers, product and design
• Team members are all data engineers but from different backgrounds
• We rely on each other to deliver our best, grow together, share knowledge openly, and invest in personal development through mentoring and innovation
• Build and maintain ingestion pipelines that capture changes from application databases, APIs, SaaS and deliver clean, analytics-ready tables to our cloud data warehouse
• Design data models with proper layering that handle real-world data complexity: out-of-order events, schema evolution, late arrivals, and backfills
• Own and evolve cloud platform infrastructure — manage GCP resources (GCS, Dataflow, Dataproc), provision and maintain environments with Terraform, and ensure the platform is cost-efficient and scalable
• 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
• Operate and improve our orchestration layer — scheduling, retries, SLA tracking, and observability for data pipelines
• Define and raise the bar on engineering standards — code quality, testing, CI/CD, documentation, and infrastructure-as-code
• Evaluate and adopt new technologies that help the team achieve its goals across data management, analytics, and machine learning
• Incorporate AI into platform services — enable AI-assisted development workflows and build internal AI backend services as part of the data platform offering
• Communicate across domains — work closely with analytics, product, compliance, and engineering teams; translate between technical and business language
• Mentor and grow with the team — share what you learn, support others, and contribute to a culture of honest technical discussion