Sr. Analytics Data Platform Engineer (Snowflake)

TL;DR

Sr. Analytics Data Platform Engineer (Snowflake/Python): Designing and maintaining a contract-driven data consolidation platform to ingest and transform billions of records daily with an accent on platform abstraction, API-first architecture, and self-service enablement. Focus on building a translation engine for YAML contracts into dbt models and Airflow DAGs, optimizing Snowflake performance, and integrating AI agents into the development workflow.

Location: Hybrid in New York City (NYC Global HQ)

Salary: $107,000 – $212,000

Company

A leading software platform providing digital media measurement, data, and analytics for the world's largest brands, publishers, and digital ad platforms.

What you will do

• Design and maintain a YAML-based "Contract" system for defining data entities, transformations, and SLOs.
• Develop a translation engine to automate the creation of dbt models, Airflow DAGs, and Snowflake objects.
• Transition the platform to a dynamic, API-first architecture to enable programmatic creation of data artifacts.
• Build and optimize data pipelines processing billions of records daily across consolidation and semantic layers.
• Develop a Python-based Contract Interpreter using Pydantic and Jinja2 for automated deployment across environments.
• Lead integrations with major social platforms (YouTube, TikTok, Meta, etc.) to measure end-to-end ad performance.

Requirements

• Must be based in or able to work hybrid from New York City.
• 5+ years of experience in Data Engineering or a related role.
• Advanced SQL skills, including performance tuning and complex transformations at scale.
• Proficiency in Python for building libraries and automation tooling.
• Deep expertise in Snowflake (schema design, Snowpipe, optimization) and dbt.
• Experience with Airflow/Cloud Composer and GCP (GCS, BigQuery, Kubernetes).

Nice to have

• Experience with configuration-driven data platforms (YAML/JSON).
• Proficiency in Looker and LookML for building semantic models.
• Experience with Kafka, schema registries, and streaming data.
• Knowledge of Terraform and data mesh principles.
• Experience building AI agent context files or meta-repo patterns.

Culture & Benefits

• Competitive compensation including eligibility for bonus, commission, and equity.
• Comprehensive benefits package.
• Use of advanced AI-assisted development tools like Claude Code, Cursor, and GitHub Copilot.
• Inclusive work environment that encourages applications from diverse backgrounds.