Senior Data Engineer

Role Description

We're looking for a Senior Data Engineer to build and scale the data infrastructure that powers our business intelligence platform. You'll work with massive volumes of ad server data, CRM data, and a variety of ancillary data sources to power real-time analytics by designing, maintaining, and enhancing pipelines that process billions of events.

This role sits at the intersection of ad-tech domain expertise and modern data engineering. You'll build systems that ingest data from SSPs, DSPs, ad servers, and various other data sources, transform it into actionable insights, and power dashboards that help our internal and external customers optimize campaigns, manage workflows, and drive efficiency and growth for everyone.

Qualifications

• 5+ years of professional data engineering experience
• 2+ years working with programmatic advertising and ad-tech data (GAM)
• Strong proficiency in Python and SQL
• Hands-on experience with data warehouses (BigQuery, Snowflake, Redshift, ClickHouse)
• Experience with batch processing frameworks (Spark, dbt, Airflow)
• Understanding of ad-tech data structures: bid requests, impressions, OpenRTB schemas, log-level data
• Experience with cloud platforms (AWS, GCP) and infrastructure-as-code
• Ability to work partial US work hours (available until 2pm ET)

Requirements

• Data Sources (Familiarity Required):
• SSP reporting APIs and log-level data (Magnite, PubMatic, Index Exchange, Xandr)
• Google Ad Manager (GAM) reporting, Data Transfer files, and BigQuery exports
• Data Models & Metrics:
• Revenue metrics: CPM, eCPM, RPM, fill rate, win rate, bid density
• Auction analytics: bid landscape, floor optimization, timeout analysis
• Demand partner performance: SSP/bidder scoring, latency analysis, viewability metrics, reach/frequency, IVT
• Supply chain data: ads.txt validation, sellers.json mapping, SPO analysis
• Technical Stack:
• Languages: Python, SQL, Scala (nice to have)
• Processing: Apache Spark, dbt, Apache Flink
• Warehouses: BigQuery, Snowflake, ClickHouse, Redshift
• Orchestration: Airflow, Dagster, Prefect
• Storage: S3, GCS, Delta Lake, Parquet
• Infrastructure: AWS, GCP, Terraform, Kubernetes

Benefits

• People-first management with minimal bureaucracy
• A friendly company culture, proven by employees who choose to return
• Flexible working hours
• 29 days of PTO (18 working days per year plus all national holidays)
• 10 paid recovery days
• Full financial and legal support for independent contractors
• Free English classes, with native speakers or Ukrainian teachers
• Dedicated HR support

Your day-to-day in this position

• Design and build and enhance scalable data pipelines for processing data from a variety of ad server and ancillary data sources
• Ingest and normalize data from multiple SSPs, DSPs, ad servers (GAM), and third-party reporting APIs
• Develop ETL/ELT workflows to transform raw data into analytics-ready datasets
• Create data models for campaign performance optimization, supply analysis, supply allocations, and profit maximization
• Build and maintain data warehouses and lakes optimized for ad-tech query patterns
• Implement data quality monitoring, validation, and anomaly detection for data streams
• Support Executive, BI and analytics teams with clean, documented, and performant data infrastructure
• Ensure data privacy compliance (GDPR, CCPA) in data collection, storage, and processing

Our next steps

• ✅ Intro call with a Recruiter
• ✅ Introductory interview with the Client
• ✅ Home assignment
• ✅ Final client interview
• ✅ Offer