Define platform architecture. Design and evolve shared platform services (data ingestion, orchestration, transformation, metadata, and observability) that balance scale, cost, and operational simplicity.
Build core infrastructure. Implement and own platform features (e.g., transformation frameworks, feature stores, real-time ingestion pipelines, lineage and observability) that power analytics and ML.
Drive data quality & observability. Champion observability, governance, and data quality standards so every dataset is trustworthy and measurable.
Enable developers. Improve the developer experience for data producers and consumers—tooling, templates, docs, and CI/CD for data assets.
Lead through influence. Mentor platform and product engineers, evangelize best practices across teams, and partner with analytics, ML, and product to prioritize platform investments.
Balance tradeoffs. Make pragmatic architecture decisions and articulate tradeoffs between speed, reliability, and maintenance cost.

Requirements

8+ years of experience designing, building, and scaling data systems—spanning pipelines, warehouses, and analytical data products that drive measurable business impact.
Proven technical leadership in architecting and evolving complex data ecosystems, including ownership of data models, transformation frameworks, and integrations across multiple data domains; advanced proficiency in SQL and Python.
Deep experience with modern data stacks, including cloud-native warehouses (e.g., BigQuery or Snowflake), orchestration tools (Airflow or equivalent), and transformation frameworks (dbt or similar).
Strong system design and architecture mindset, able to reason about scalability, cost, and performance trade-offs, and define long-term data strategy.
Exceptional collaboration and influence skills, partnering effectively with Marketing Engineering, Analytics, and Data Science to translate business goals into robust, production-grade data systems.
Strong sense of ownership and accountability, balancing hands-on technical execution with the ability to mentor others, raise standards, and drive organization-wide improvements in data quality and reliability.
functional counterparts (design, product, data science).
Commitment to building inclusive teams and team culture.

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard skills

data ingestiondata transformationdata orchestrationmetadata managementobservabilitySQLPythondata modelingtransformation frameworkscloud-native data warehouses

Soft skills

technical leadershipcollaborationinfluencementorshipownershipaccountabilitycommunicationproblem-solvingstrategic thinkingteam building