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MediaRadar, now including the data and capabilities of Vivvix, powers the mission-critical marketing and sales decisions that drive competitive advantage. Our competitive advertising intelligence platform enables clients to achieve peak performance with always-on data and insights that span the media, creative, and business strategies of five million brands across 30+ media channels. By bringing the advertising past, present, and future into focus, our clients rapidly act on the competitive moves and emerging advertising trends impacting their business. We are seeking an experienced Data Engineer to join the team and take ownership of our data infrastructure. With millions of new data points ingested daily, your mission will be to architect, build, and scale robust data pipelines that ensure flawless data quality. You'll work with a passionate team on a modern cloud data stack (Azure, Databricks), solving complex challenges to deliver timely and reliable data that drives our business and delights our customers.
Job Responsibility:
Architect & Build: Design, implement, and optimize scalable and reliable ELT/ETL pipelines using Databricks, Spark, Python and Store Procedures. You will take the lead in orchestrating complex workflows using Apache Airflow, ensuring seamless dependency management across our cloud environment
Ensure Data Integrity: Develop and implement comprehensive testing frameworks, data validation rules, and QA plans to guarantee the accuracy and integrity of our data assets
Optimize & Troubleshoot: Proactively monitor system performance, tune complex SQL queries, and troubleshoot production issues. You will perform root-cause analysis and implement lasting solutions to improve system health and reliability
Collaborate & Innovate: Actively participate in an Agile environment (Scrum, sprints, backlog grooming) and collaborate with cross-functional teams. You'll help evaluate and introduce new technologies and best practices to continuously improve our data platform
Requirements:
A bachelor’s or master’s degree in computer science, Engineering, or a related field (or equivalent practical experience)
5+ years of hands-on experience in data engineering, with a strong focus on Databricks, Apache Spark and 1 RDBMS solution for processing large-scale datasets
Proven experience with Apache Airflow for workflow orchestration, including designing DAGs, managing task dependencies, and integrating with Azure-based services
Expert-level proficiency in Python (including PySpark) and a strong understanding of data structures, algorithms, and OOP principles
Deep expertise in SQL and Spark SQL, with proven experience writing and optimizing complex analytical queries
Hands-on experience with the Databricks ecosystem, including Delta Lake, Unity Catalog, and Data frame APIs
Nice to have:
Experience building and maintaining CI/CD pipelines using tools like Azure DevOps
Experience with Managed Workflows for Apache Airflow (MWAA) or running Airflow on Kubernetes
Previous experience migrating a legacy data system to a modern, unified data platform
Experience working in a fast-paced, product-driven environment