Manager, Decision Scientist and Engineering

Overview and Responsibilities

The Signal Intelligence team works with high-volume, multi-source data ecosystem and builds trusted, decision-ready data products. We work across operational systems, client app event streams, ad tech platforms, stitcher events, ad server logs, and telemetry — stitching them together into the data marts and dashboards that power business, product, and ad ops decisions.

Responsibilities

• Build and own data marts spanning operational, advertising, and telemetry data — designed for analytics, reporting, AI, and operational use cases
• Ingest and process large-volume event data from client apps, ad tech platforms, stitcher services, ad servers, and telemetry pipelines
• Clean, harmonize, and integrate data across systems with different schemas, identifiers, grains, and timing — producing conformed dimensions and shared definitions (users, sessions, devices, content, campaigns, impressions)
• Sew identity and sessions across client, server, and ad-side events to enable accurate user, content, and revenue analytics
• Troubleshoot data incidents end-to-end — from a dashboard anomaly back through marts, transformations, and raw event logs — and drive permanent fixes
• Build, support and improve visualizations in partnership with analysts and stakeholders, ensuring dashboards are accurate, performant, and trusted
• Establish data quality standards — testing, monitoring, alerting, freshness and volume SLAs — so issues are caught before stakeholders see them
• Document datasets, lineage, and business logic so consumers across analytics, product, and ad ops can self-serve with confidence
• Partner closely with analysts, data scientists, ad ops, product, and source-system owners to translate business questions into durable data models
• Develop/Improve new or underutilized data sets internally and externally
• Analyze complex and huge datasets to
  • understand patterns and develop actionable insights
  • develop new initiatives to improve business KPIs such as usage, revenue, etc.
  • define new metrics and KPIs to track new initiatives
• Work closely with all business functions to enable transparent data-based decision making
• Contribute to the daily variance identification across multiple platforms
• Drive complex strategic projects investigations and analysis
• Work cross functionally on enterprise-wide programs with Engineering, Broadcast Operations, Finance, BI and Data Engineering teams to improve performance and profitability
• Research and share information on the latest tools and best practices
• Mentor engineers and analysts on SQL, modeling, event data, and engineering best practices

Qualifications

• BA/BS in Computer Science, Math, Physics, Engineering, Economics, Statistics or related technical field
• 5+ years of data engineering experience building production pipelines and data models
• Expert SQL skills, including performance tuning on large, event-scale datasets
• Strong experience with a cloud warehouse / lakehouse (Snowflake, BigQuery, or Databricks)
• Experience working with JSON, Parquet, etc. types of files
• Proficient in Python for data processing and pipeline development
• Experience with dbt (or equivalent transformation framework)
• Experience with orchestration tools (Airflow)
• Hands-on experience with high-volume event data — clickstream, telemetry, ad impressions, or similar — including deduplication, late-arriving data, sessionization, and schema evolution
• Deep understanding of dimensional modeling, star/snowflake schemas, slowly changing dimensions, and data mart design
• Proven track record harmonizing data across multiple source systems with conflicting schemas, identifiers, or grain
• Experience debugging data quality issues across the full stack — from BI tool to warehouse to raw event logs
• Comfort working directly with BI tools (DOMO, Looker, Mode) — both consuming them and supporting their development
• Strong analytical and logical skills

Additional Qualification

• MS in quantitative discipline or equivalent experience
• Experience leading engineering or operations teams
• Understanding of statistical analysis using R and predictive analytics tools, including ability to define, complete and present analysis.