You will work closely with product, platform, and business teams, helping them use data effectively while ensuring that our systems remain maintainable and trustworthy grows.
Your responsibilities:
Own the design, delivery, and operation of complex data pipelines, datasets, and platform components.
Translate business and analytical requirements into scalable data models, reliable data products, and clear technical plans.
Design and evolve data architecture, storage, processing, and orchestration patterns for large-scale workloads.
Improve data quality, observability, lineage, and incident response for critical datasets and pipelines.
Investigate and resolve challenging performance, reliability, and data-correctness issues in production.
Establish reusable tools, conventions, and automation that improve engineering productivity and reduce operational risk.
Work with product teams and business stakeholders to define data contracts, priorities, and success criteria.
Contribute to technical direction through design reviews, thoughtful trade-offs, and documentation.
Support and mentor other engineers through reviews, pairing, and knowledge sharing.
Participate in the on-call rotation and take ownership of improving the operational health of the systems you support.
5+ years of experience in data engineering, backend engineering, or a related role; or equivalent experience delivering and operating production data systems.
Proven experience independently delivering complex data pipelines or data-platform capabilities from problem definition through production operation.
Strong Python and SQL skills, including writing maintainable production code and optimizing non-trivial queries.
Hands-on experience with workflow orchestration tools such as Airflow, Prefect, or Dagster.
Strong understanding of data modeling, including designing maintainable analytical models and data contracts for multiple consumers.
Solid knowledge of data architectures and storage systems, including the trade-offs between different processing and storage approaches.
Experience designing for reliability: testing, monitoring, data-quality validation, alerting, debugging, and incident resolution.
Ability to make technical decisions under ambiguity, explain trade-offs clearly, and collaborate effectively with engineers and non-technical stakeholder
