Senior Analytics Engineer – Media Forecasting & Data Systems

BIA Advisory Services is seeking a Senior Analytics Engineer – Media Forecasting & Data Systems to help build, maintain, and advance the data infrastructure behind BIA’s industry-leading local advertising forecasts and market intelligence products.
Working across television, radio, streaming, digital, and emerging media channels, this role will support the systems and analytical workflows used to collect, organize, transform, validate, analyze, and publish BIA’s forecasting data across broadcast television, radio, streaming, digital, and emerging media channels.
The position combines elements of analytics engineering, data engineering, database development, forecasting support, and broadcast and digital media analytics. The successful candidate will work closely with the Vice President of Forecasting & Data Analysis to translate research methodologies, business assumptions, and media industry knowledge into reliable, scalable, repeatable, and well-documented technical processes.
This is not solely a traditional IT or software engineering role. The ideal candidate will understand the analytical purpose behind the data, recognize when outputs do not appear reasonable, investigate inconsistencies, and collaborate effectively with researchers, analysts, and business stakeholders.
Experience working with digital and traditional media, local advertising, audience measurement, media research, advertising data, or related industry analytics is highly desirable. Tips: Provide a summary of the role, what success in the position looks like, and how this role fits into the organization overall.
Key Responsibilities
Data Infrastructure and Database Management
Maintain and improve database tables, views, schemas, queries, and data structures supporting BIA’s forecasting and analytical processes.
Design database structures that improve accessibility, consistency, scalability, and reliability of forecast inputs and outputs.
Help migrate spreadsheet-based and manually managed processes into standardized database workflows.
Monitor database dependencies and help resolve data-loading, performance, access, and data-quality issues.
Maintain clear relationships between source data, intermediate tables, forecast models, analytical outputs, and published deliverables.
Support the organization and management of media, advertising, audience, market, and geographic datasets.
Data Pipelines and Process Automation
Build and maintain repeatable processes for ingesting data from internal and external sources.
Develop, maintain, and troubleshoot R, Python, and SQL scripts used for data preparation, forecasting, validation, reporting, and automation.
Automate recurring data-loading, transformation, reconciliation, quality-control, and output-generation tasks.
Reduce reliance on manual processes, hard-coded file paths, and one-time workflows.
Improve the ability to rerun workflows safely when source data, assumptions, or forecasting methodologies change.
Establish appropriate logging, error handling, process controls, and workflow monitoring.
Support integration of data received from media research providers, vendors, partners, APIs, spreadsheets, regulatory sources, and other external datasets.
Forecasting and Analytical Support
Work with the Vice President of Forecasting & Data Analysis to translate forecasting methodologies and research assumptions into transparent and maintainable code.
Support forecasting and analytical processes across television, radio, streaming, digital, and emerging local media channels, incorporating audience measurement, advertising revenue, and market intelligence datasets into forecasting workflows.
Prepare forecast inputs and outputs for methodological review, business analysis, and publication.
Help investigate unexpected results, data discrepancies, outliers, and changes in historical trends.
Apply knowledge of media industry data, audience measurement, advertising trends, and market dynamics to improve forecast quality and identify anomalies.
Ensure market-level, media-level, category-level, and national forecast outputs reconcile appropriately.
Support statistical, time-series, econometric, or Bayesian modeling workflows as needed.
Maintain a clear separation between source data, assumptions, modeled results, and final forecast adjustments.
Data Quality and Validation
Develop automated and manual quality control procedures for major data and forecasting workflows.
Validate that source totals, database values, market allocations, historical estimates, and published outputs reconcile.
Create checks for missing data, duplicate records, unusual year-over-year changes, mapping errors, and incomplete loads.
Investigate data inconsistencies and determine whether issues originate from source data, transformation processes, modeling assumptions, or output generation.
Maintain documentation of data limitations, assumptions, exceptions, and known issues.
Help establish consistent review procedures before data are incorporated into BIA products or delivered to clients.
Documentation and Business Continuity
Maintain documentation of data sources, database tables, scripts, dependencies, refresh schedules, assumptions, and output locations.
Expand and maintain BIA’s Data Management and Forecasting Workflows inventory.
Document how critical processes are executed, reviewed, corrected, and published.
Develop sufficient familiarity with key workflows to provide reliable secondary coverage.
Help ensure forecasting and data-management knowledge is distributed and transferable rather than dependent on a single individual.
Support onboarding of future analysts, engineers, and researchers working with BIA’s forecasting systems.
Cross-Functional and Industry Collaboration
Collaborate with Research, Content, Product, and Sales teams to understand how forecast outputs are used in reports, presentations, platforms, and client-facing analysis.
Work with internal teams to ensure database outputs can be effectively incorporated into BIA products.
Evaluate data received from external media research providers, audience measurement companies, industry organizations, and research partners.
Help establish efficient and secure processes for receiving, storing, transforming, documenting, and maintaining partner data.
Communicate technical issues, process risks, and recommend improvements.
Develop familiarity with digital and traditional media and local media business models, advertising sales, audience measurement, and market intelligence.
Required Qualifications
Bachelor’s degree in data science, computer science, statistics, economics, information systems, analytics, or related discipline, or equivalent professional experience.
Understanding digital and traditional media and/or local media business models, advertising sales, media planning, audience measurement, and/or market analysis.
Four or more years of experience in analytics engineering, data engineering, database development, statistical programming, or related fields.
Familiarity with media industry datasets such as Nielsen, Comscore, Kantar, Standard Media Index (SMI), FCC ownership data, MediaRadar, or similar sources.
Advanced SQL skills and experience working with relational databases.
Strong experience using R or Python for data transformation, analysis, automation, or workflow development; willingness to develop proficiency in both languages.
Strong data validation, reconciliation, debugging, and analytical problem-solving skills.
Ability to translate business and analytical requirements into reliable, documented technical processes.
Strong written communication, documentation, and collaboration skills.
Preferred Qualifications
Experience supporting television, radio, streaming, cable, digital advertising, media research, audience measurement, or local advertising analytics.
Experience working with station-level, market-level, geographic, audience, or advertising revenue datasets.
Experience building or maintaining ETL/ELT processes involving multiple data sources.
Experience supporting forecasting, time-series analysis, statistical modeling, econometric, or Bayesian workflows.
Experience with MariaDB, MySQL, PostgreSQL, or comparable relational database platforms.
Experience with R Markdown, Quarto, Jupyter, or similar reproducible reporting tools.
Familiarity with economic, demographic, media, advertising, and market-level datasets.
Familiarity with business intelligence and data visualization platforms.
Experience working with data from APIs, Excel workbooks, CSV files, vendor platforms, regulatory filings, and other third-party data providers.
Ability to work independently, manage

Work arrangement
Yes