Data Engineering Team Lead

Entravision Communications Corporation•Austin, TX

10d•Onsite

About The Position

Entravision is a leading global advertising, media and ad-tech solutions company connecting brands to consumers by representing top platforms and publishers. Our service portfolio enables high-performance campaigns while using highly competitive audience reach, cutting-edge mobile programmatic solutions, machine-learned bidding algorithms and demand-side platforms on a global scale. Smadex is a leading advertising technology company founded in Barcelona in 2011 and sold to American-based and stock-listed Entravision in 2018 (NYSE::EVC). We are one of the top mobile ad-tech companies in the world and the largest Demand Side Platform (DSP) based in Europe, with our revenues over +$100M and consistently growing +40% YoY over the past years. Our mission is to continue to improve our ad-tech platform to help our clients achieve their programmatic advertising campaign goals, and we want to give our employees a job they’ll love, where they will be challenged to improve results through real-life engineering and data analysis and where everyone’s implication has an impact. Data Engineering Team Lead Austin, TX | Full Time We are looking for a highly skilled Data Engineering Team Lead with a deep background in big data architectures and a proven track record of leading high-performing technical teams. You should be passionate about extreme performance and excited by the challenge of managing massive data streams. Your success will be measured by your ability to scale the engineering organization, design robust data quality frameworks, and deliver the infrastructure required for advanced machine learning feature serving. This is a full-time position based in Austin, TX, or New York, NY

Requirements

6+ years of experience in software or data engineering, with at least 2 years in a formal leadership or management role building high-throughput distributed systems.
Advanced knowledge of Spark and the ability to conduct deep code reviews.
Proven track record of designing and optimizing complex batch and streaming pipelines using Apache Spark.
Advanced SQL knowledge and experience with cloud environments (AWS), particularly EMR, EC2, Athena, and S3.
Strong ability to debug complex distributed systems and drive root-cause analysis for production issues.
Excellent communication skills in English, with the ability to translate technical complexity for stakeholders.

Nice To Haves

Previous experience in the AdTech industry or working with Real-Time Bidding (RTB) ecosystems, ideally Demand Side Platforms (DSP) side.
Direct experience building Feature Stores or working closely with Data Science teams on the ML lifecycle.
Experience with containerization and orchestration (Docker, Kubernetes) at scale.
Master’s degree in Computer Science, Engineering, or Applied Mathematics.

Responsibilities

Lead and scale two specialized sub-teams (Data Quality and Feature Engineering), managing performance, career development, and hiring.
Design scalable, highly available data pipelines bridging big data storage and real-time machine learning inference.
Establish rigorous standards for data observability, implementing automated monitoring and alerting for complex data ingestion services.
Define and enforce policies for data privacy, security, and lifecycle management, ensuring compliance with relevant regulations and company standards.
Oversee the development and optimization of high-throughput features using Scala to aggregate data for our Redis Cluster.
Coordinate with the infrastructure team ensuring the execution of complex data workflows and DAGs using Apache Airflow.
Monitor global deployments and ensure the stability, reliability, and performance of production systems following a "you build it, you run it" philosophy.
Partner with the CDO to align technical strategies with business objectives and Machine Learning roadmaps.
Collaborate closely with the CTO for technical solutions and strategy, and partner with the CPO on product integration and transversal projects.