What You'll Actually Do:
Lead and mentor a team of Data Engineers, fostering a culture of technical excellence, accountability, and collaboration.
Define and drive the teams technical roadmap, standards, and best practices for data modeling, performance, and reliability.
Design, build, and evolve scalable embedding pipelines and recommendation infrastructure, from algorithm tuning and training workflows to production deployment.
Manage and optimize vector db, ensuring efficient indexing strategies, search performance, and low-latency retrieval.
Lead the development of retrieval services leveraging Approximate Nearest Neighbor (ANN/KNN) techniques such as HNSW, IVF, and Product Quantization (PQ).
Ensure reliability, observability, and scalability across all real-time data systems powering our recommendation engine.
Collaborate with Data Science, Product, and Analytics to operationalize models, validate KPIs, and translate technical insights into actionable product improvements.
3+ years of experience leading data engineering teams.
5+ years of hands-on experience developing large-scale data and ML systems, with strong foundations in distributed computing, data pipelines, and algorithm development.
Strong programming skills in Python, with practical experience using modern data and ML frameworks such as Spark, Airflow, MLflow, or equivalent technologies.
Proven experience designing and maintaining embedding pipelines, feature stores, and model-serving infrastructure.
Excellent leadership and communication skills, with a passion for mentoring engineers and driving technical excellence.
Ability to translate complex technical challenges into scalable, reliable engineering solutions.
