Sr. Analytics Data Platform Engineer at DoubleVerify | AdTechTalent
Engineering17 months agoHybrid
DoubleVerify
Sr. Analytics Data Platform Engineer
seniordata engineeringSQLPythonSnowflakedbtAirflowGCPLookerLookMLKafkaTerraformCI/CDAI-assisted developmentcontract-driven data platformdata pipelinesdata warehousingclouddata qualityobservability
Key details
Salary
$107K – $212K
Employment type
Full-time
Seniority
Senior
Years experience
5-10
Location
New York, US
Full job description
Senior Analytics Data Platform Engineer role at DoubleVerify in New York (Hybrid). Responsible for designing and maintaining a YAML-based contract system for data entities and transformations, developing translation engines for automated dbt models, Airflow DAGs, and Snowflake objects, transitioning to an API-first architecture, enabling self-service data solutions, optimizing performance and scale, and acting as product manager for the platform. Build data pipelines processing billions of records daily, develop Python libraries for contract interpretation, lead integrations with major social platforms, maintain semantic layers with LookML, implement observability and monitoring, leverage AI tools for development, design schema evolution and migration strategies, collaborate in agile teams, and mentor engineers. Requires 5+ years in data engineering, strong SQL and Python skills, deep Snowflake and dbt experience, orchestration tools knowledge (Airflow), cloud platform experience (GCP), data warehousing expertise, CI/CD pipeline experience, and familiarity with AI-assisted development tools. Preferred experience includes contract-driven data platforms, Looker/LookML, Kafka, data quality frameworks, Terraform, and data mesh principles. Salary range $107,000 - $212,000 plus bonus, equity, and benefits.
What you'll do
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Design and maintain 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 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 leveraging Snowflake/dbt stack
Act as Product Manager for 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 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 analytics
Implement and maintain observability with 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 including versioning, backward compatibility, and large-scale backfills
Work in multi-functional agile teams with end-to-end product development responsibility
Collaborate with engineers from partner platforms on API development and data integration
Train and mentor software engineering team
Requirements
Bachelor's degree or foreign equivalent in Computer Science, Data Engineering, or related field
5+ years of experience in 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 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
