Software Engineer - Data Platform & Data Products

We are looking for a Senior Engineer to help us transition from centralized data management to a scalable, self-service data mesh. In this role, you won't just move data, you will build the software and services that make data discoverable, reliable, and "product-ready" for the entire organization. As an engineer focused on data platform, you will operate at the intersection of backend engineering and data engineering. You will design and own data products delivered through software, including curated datasets, data services, and APIs that power analytics, applications and AI/ML use cases. This includes building batch and real-time streaming pipelines, backend services, and developer-facing tools. You’ll drive architecture, system design, and engineering standards while working closely with data, platform, and product teams. While a DevOps team supports infrastructure, you will be hands-on with cloud-native systems and own services end to end. Design, build and maintain frameworks, tooling and governance for data platform services enabling self service data product development Design and deliver data products with clear contracts, SLAs, and high performance access patterns Develop high-throughput batch and real-time streaming pipelines that are resilient and observable Apply rigorous software engineering practices (unit/integration testing, CI/CD, design patterns) to the data domain Implement automated data quality, lineage, and access controls as core platform capabilities Tune complex SQL, optimize storage formats (Iceberg/Parquet), and manage distributed system bottlenecks Partner with platform/DevOps teams on cloud-native infrastructure while maintaining ownership of service-level operations 7+ years of experience in software/data engineering, with focus on data architecture Strong programming skills in Python and/or Java/Scala/Go Proficiency in data modeling, SQL, partitioning strategies, and query optimization Hands-on experience with modern data stack (e.g., Flink, Kafka, dbt, Airflow, Spark, Iceberg or similar) Experience with Open Table Formats (e.g., Apache Iceberg, Delta Lake) and catalog management Solid experience building APIs, services, and distributed systems Proven track record of implementing CI/CD, automated testing, data quality validation, and observability (logging, metrics, tracing) for data systems Experience with cloud and containerized systems (e.g., AWS/GCP, Kubernetes, Docker) Experience building internal data platforms or developer-facing tools Solid experience with data mesh and “Data as a Product” principles Experience building the data foundations for GenAI or ML workflows Knowledge of data governance, lineage, and metadata systems

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