Sr. Data Engineer

Paramount•New York, NY

1d•$146,160 - $219,240

About The Position

The Signal Intelligence team works with a 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. We are currently looking for a Senior Data Engineer to develop processes, methodologies and systems to consolidate and analyze unstructured diverse data sources that generate breakthroughs, actionable insights and solutions that inform decisions and business direction. This person will own the full lifecycle of our data — from raw event ingestion through harmonization, architecting, designing, modeling, visualization, and incident response. In addition, the successful candidate will lead development of software algorithms to structure, analyze and leverage data in the development of product and applications. Candidates should possess extensive experience in Data Engineering, Data Analysis, and Predictive Analytics, and be able to independently develop and communicate complex statistical analysis and insight to peers and senior leadership. We are looking for someone who can work effectively in a matrixed organization, lead data-based decision making, and bring clear data-driven insights to existing business operations.

Requirements

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

Nice To Haves

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.

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)
Stitch 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., and 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