Staff Data Engineer

Fox-posted 4 days ago
Full-time • Mid Level
Hybrid • New York, NY
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The Staff Data Engineer serves as a technical and strategic leader responsible for building, optimizing, and governing the company’s data and analytics ecosystem. This includes designing robust data pipelines, developing semantic layers and metrics frameworks, and ensuring that business intelligence platforms deliver trusted, consistent, and timely insights. In this role, you will bridge the gap between raw data and business outcomes — enabling teams across Content, Ad Sales, Marketing, and Finance to make data-driven decisions at scale.

  • Design and build scalable ETL/ELT pipelines using tools like Databricks, DBT, Matillion, and Airflow to process structured and unstructured data.
  • Develop data models and transformations for analytics use cases (star schema, data vault, or lakehouse).
  • Optimize data processing for performance, reliability, and cost efficiency across Databricks, Redshift, and Snowflake environments.
  • Implement real-time and batch data ingestion from streaming, ad tech, and operational systems.
  • Lead the design and development of semantic layers that power business reporting (Looker, Tableau, Power BI, or ThoughtSpot).
  • Establish metrics standardization and a unified business glossary to ensure consistent definitions across departments.
  • Build automated data models, dashboards, and data marts for stakeholders such as Ad Sales, Research, and Content Strategy.
  • Partner with Analytics and Finance teams to deliver executive-level reporting (KPI frameworks, performance scorecards, and forecasting inputs).
  • Implement BI performance optimization: caching, query tuning, and dashboard load improvements.
  • Enforce data validation, lineage, and observability frameworks using tools like Great Expectations, Monte Carlo, or Databricks Unity Catalog.
  • Ensure all datasets and dashboards meet governance, compliance, and privacy standards (GDPR, CCPA, internal policies).
  • Define data quality SLAs and implement automated monitoring for critical pipelines and BI assets.
  • Drive adoption of metadata management, documentation, and versioning best practices across data and BI environments.
  • Partner with Product, Finance, AdTech, and Data Science teams to translate business requirements into scalable data and BI solutions.
  • Collaborate with BI developers, analysts, and data scientists to ensure models and datasets are optimized for analytics and experimentation.
  • Serve as the technical liaison between the Data Engineering and Analytics teams, ensuring efficient data flow and model usability.
  • Participate in data design reviews, architecture sessions, and governance boards to align technical decisions with business priorities.
  • Define the technical roadmap for data and BI engineering, aligning with enterprise data strategy and architecture.
  • Mentor engineers and analysts on best practices for SQL optimization, data modeling, and visualization design.
  • Champion data democratization — empowering users through self-service capabilities and well-structured data models.
  • Evaluate emerging technologies (semantic layer tools, AI-powered analytics, agentic data automation) to advance the BI ecosystem.
  • 8+ years of experience in Data Engineering, BI Engineering, or Data Architecture roles.
  • Expert in SQL, Python, and Spark (PySpark or Scala).
  • Hands-on experience with Databricks, Redshift, Snowflake, or BigQuery.
  • Proficiency with data modeling (fact/dimension, star schema, lakehouse).
  • Experience with workflow orchestration tools (Airflow, Dagster, or Prefect).
  • Advanced understanding of BI tools — Looker (LookML), Tableau, Power BI, or equivalent.
  • Familiarity with semantic modeling, data marts, and metrics governance.
  • Solid experience with version control (Git), CI/CD for data, and testing frameworks.
  • Proven ability to balance engineering depth with analytical accessibility — building systems that both scale and empower non-technical users.
  • Experience in media, streaming, or digital advertising data ecosystems.
  • Bachelor’s or Master’s degree in Computer Science, Engineering, or related quantitative field.
  • Familiarity with ad delivery data (Freewheel, Google Ad Manager, Innovid) and audience metrics (Nielsen, Comscore).
  • Understanding of machine learning feature pipelines or real-time audience segmentation.
  • Experience implementing DBT Cloud, Databricks Unity Catalog, or ThoughtSpot Modeling Language (TML).
  • medical/dental/vision
  • insurance
  • a 401(k) plan
  • paid time off
  • annual discretionary bonus
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