Sr. Analytics Data Platform Engineer

What you'll do

• Design and maintain the YAML-based 'Contract' system for defining data entities, transformations, and SLOs
• Develop translation engine converting user contracts into automated dbt models, Airflow DAGs, and Snowflake objects
• Transition platform to a dynamic, API-first architecture for programmatic creation of data artifacts
• Build tooling and guardrails for self-service deployment of data solutions with governance and security
• Optimize translation layer for efficiency, cost-effectiveness, and leverage Snowflake/dbt stack
• Act as Product Manager for the platform, gathering feedback to simplify data development lifecycle
• Design and build data pipelines processing billions of records daily using contract-driven architecture
• Develop and extend Contract Interpreter Python library to generate dbt models, Airflow DAGs, and environment configurations
• Lead new initiatives and integrations with major social platforms to measure ad performance end-to-end
• Build and maintain semantic layer with LookML models, explores, and views for customer-ready analytics
• Implement and maintain observability including monitoring, alerting, watermarking, and data consistency checks
• Leverage AI agents and tooling to accelerate development, automate workflows, and encode institutional knowledge
• Design schema evolution and data migration strategies managing versioning, compatibility, and deployments
• Work in multi-functional agile teams with end-to-end responsibility from contract definition to customer-facing data
• Collaborate with engineers from partner platforms on API development and data integration specifications
• Train and mentor a team of software engineers

Requirements

• Bachelor's degree or foreign equivalent in Computer Science, Data Engineering, or a related field
• 5+ years of experience in a Data Engineering or related role
• Strong SQL skills including advanced querying, performance tuning, window functions, and complex transformations at scale
• Proficiency in Python including building libraries, data processing scripts, and automation tooling
• Experience with Pydantic, Jinja2, or similar templating frameworks is a plus
• Deep experience with Snowflake including schema design, Snowpipe, streams, tasks, materialized views, clustering, and query optimization
• Experience with dbt (data build tool) including building and maintaining models, macros, custom materializations, and incremental strategies
• Experience with orchestration tools such as Airflow / Cloud Composer including DAG design, scheduling, and monitoring
• Experience with cloud platforms such as GCP (GCS, BigQuery, Cloud Composer, Kubernetes) or equivalent
• Strong understanding of data warehousing concepts including dimensional modeling, star/snowflake schemas, slowly changing dimensions, fact/aggregate table design, and data consistency patterns
• Experience with CI/CD pipelines such as GitLab CI, Flyway migrations, or similar deployment automation
• Experience with AI-assisted development tools such as Claude Code, Cursor, GitHub Copilot or similar AI coding assistants
• Experience building or contributing to AI agent context files (AGENTS.md), skills, or meta-repo patterns is a strong plus

Tech stack

Benefits