Participate in the design and lead the implementation and optimization of data models and pipelines for multiple uses; understand and implement the data architecture and security and access standards used within the Product organization
Understand and lead the implementation of data management standards for schema evolution, versioning, backfill and retry capabilities, etc.
Design and manage the adoption of best practices in testing, monitoring, alerting and runbook-driven operations for pipelines; in collaboration with the Data Platform Architect, champion the development and adoption of robust RCA and remediation strategies
Implement and enforce SLAs and data lifecycle management; Develop standards for data quality checks, observability, and alerting for production data systems
Contribute to technical and architectural documentation; provide guidance on data cataloguing and metadata requirements to data owners
Contribute to design reviews, raising engineering quality, and sharing best practices; Collaborate deeply with cross functional teams to align event design, source systems, and data contracts

6-8 years of progressively greater responsibilities in the areas of data engineering and architecture, ideally with a focus on product data (telemetry, logs, etc.)
Extensive experience in data architecture and data modeling, database and pipeline performance optimization, and standardization of design and implementation
Expert in the design of data flows, pipelines, and data quality and observability systems, incorporating monitoring and alerting frameworks and policies
Experienced in data cataloguing, metadata and lineage, and in working with data stewards to maintain these assets
Extensive experience incorporating devops standards (CI/CD, testing frameworks, etc.) and integrating with software engineering processes
Experience working within a data governance framework and managing data priorities across multiple stakeholders
Strong communication and collaboration
Bonus: Experience with dbt, Databricks (and Unity Catalog) git, AWS
Experience with ML data requirements

Senior Data Engineer

Key skills