Data Engineering Lead

MediaRadar

1d•Remote

About The Position

MediaRadar, an Industry Leader in Marketing Intelligence now including the data and capabilities of Vivvix, powers the mission-critical marketing and sales decisions that drive competitive advantage. Our next-generation marketing intelligence platform enables clients to achieve peak performance with always-on insights that span the media, creative, and business strategies of 5 million brands across 30+ media channels and 275 billion in media spend. Role Summary The Data Engineering Lead is a high-velocity, hands-on "player-coach" responsible for technical stewardship, designing scalable systems, and integrating complex Machine Learning models into robust ETL pipelines. You will lead a lean team through a cultural shift toward cross-trained agility while spending 70-80% of your time in the code. Success is defined by achieving total record processing, maintaining strict cloud cost-efficiency, and shrinking data delivery windows.

Requirements

10+ years of experience in Data or Software Engineering with deep codebase involvement.
3+ years as a Technical Lead managing agile teams.
Proven ability to lead lean, high-impact teams while maintaining high individual output.
Experience with cross-training advocacy and scaling data processing through automation.
Core: Python, PostgreSQL + pgvector.
Big Data: Azure Databricks, PySpark, Delta Lake
DevOps: Docker, Git, Azure DevOps, CI/CD

Nice To Haves

Workflow Orchestration: Experience with Apache Airflow.
Containerization: Familiarity with Azure Kubernetes Service (AKS).

Responsibilities

Architect and implement complex, end-to-end data pipelines using Azure Databricks and PySpark.
Design, build, and maintain a scalable data architecture using the Medallion Architecture (Bronze/Silver/Gold layers).
Optimize Apache Spark jobs, tune Databricks units, and define cluster policies to minimize compute costs.
Proactively audit and refactor pipelines every 3-6 months to maintain effectiveness and reduce cloud costs.
Implement caching strategies (e.g., broadcast joins) and manage performance impact.
Develop a proactive monitoring and alerts framework to ensure 99.9% reliability and mitigate system issues before they impact end-users.
Build an end-to-end Data Validation Framework (e.g., Great Expectations) to enforce data accuracy and consistency.
Minimize job failure rates and ensure data is available in the Gold layer within the required 24-hour turnaround time.
Architect and design high-performance schemas in PostgreSQL, managing indexing, partitioning, and optimizing complex analytical queries.
Lead a lean team toward cross-trained agility, moving away from "siloed specialists".
Manage sprint cycles, conduct code reviews, and guide the team on best engineering practices (including CI/CD).
Anticipate future data needs and design High-Velocity Architecture that is highly scalable and manageable to handle sudden volume increases (e.g., double the data from new sources like paid social/CTV).
A critical function is translating business-level requirements into clear, technical user stories for developers.
Collaborate with ML teams to integrate automated model orchestration into robust ETL pipelines.
Collaborate with the offshore team lead to facilitate seamless knowledge transfer and operational continuity across time zones.
Establish clear communication protocols, standardized documentation, and robust feedback loops to ensure alignment on project goals.
Act as the primary bridge between teams to mitigate bottlenecks and maintain high-quality delivery standards.