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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Sr Data Engineer (Data Architecture and Modeling) - **Company:** Paramount Pictures - **Location:** New York, NY, United States (Remote available) - **Experience:** Expert - **Salary:** $110,000.0 - $165,000.0 - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Airflow, Data Analysis, Business Logic, BigQuery, Clickstream, Data Architecture, Data Deduplication, Information Engineering, Data Mart, Software Debugging, Dimensional Modeling, JSON, Performance Tuning, Standard Sql, SQL Databases, Workflow Management Systems, Parquet, Snowflake, Information Technology, Domo, Looker Analytics, Data Pipelines, Databricks - **Published:** July 30, 2026 - **Apply:** https://careers.paramount.com/talentcommunity/apply/1407144800/?locale=en_US ## About the Role 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., · 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. ## Description 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., · 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 o understand patterns and develop actionable insights o develop new initiatives to improve business KPIs such as usage, revenue, etc. o 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 ## Related Videos - [From Messy Queries to Scalable Systems - How Data Engineering actually works](https://www.wearedevelopers.com/videos/100203-from-messy-queries-to-scalable-systems-how-data-engineering-actually-works) - [Tips and Tricks for Working with JSON](https://www.wearedevelopers.com/videos/1229-tips-and-tricks-for-working-with-json) - [Data Science in Retail](https://www.wearedevelopers.com/videos/586-data-science-in-retail) - [Enjoying SQL data pipelines with dbt](https://www.wearedevelopers.com/videos/823-enjoying-sql-data-pipelines-with-dbt) - [AI Model Management Life Circles: ML Ops For Generative AI Models From Research to Deployment](https://www.wearedevelopers.com/videos/1152-ai-model-management-life-circles-ml-ops-for-generative-ai-models-from-research-to-deployment) - [Introducing JSON Structure](https://www.wearedevelopers.com/videos/100219-introducing-json-structure) ## Related Articles - [Highest Paying Tech Companies for Developers](https://www.wearedevelopers.com/magazine/220-highest-paying-tech-companies-for-developers) - [Making Data Warehouses Fast: A Developer’s Story](https://www.wearedevelopers.com/magazine/107-making-data-warehouses-fast-a-developer-s-story) - [Data Engineer Salary UK](https://www.wearedevelopers.com/magazine/253-data-engineer-salary-uk) - [Top Big Data Technologies That You Need to Know](https://www.wearedevelopers.com/magazine/108-top-big-data-technologies-that-you-need-to-know) - [Coffee with Developers - Maria Apazoglou - Making AI understandable for all in production](https://www.wearedevelopers.com/magazine/475-coffee-with-developers-maria-apazoglou-making-ai-understandable-for-all-in-production) - [Data Analyst Salary in the UK](https://www.wearedevelopers.com/magazine/278-data-analyst-salary-in-the-uk)