If youre passionate about data engineering and driving business growth through insights, wed love to hear from you!
Responsibilities
Lead and grow Data Engineering and Machine Learning teams in a high-scale environment (tens of billions of events per day).
Own the design and evolution of a self-service data platform enabling internal teams to easily build, ship, and consume data products.
Architect and scale batch and streaming pipelines powering core business and ML use cases.
Drive production ML systems end-to-end (recommendation, ranking, prediction) with direct business KPI impact.
Ensure reliability, scalability, and observability of large-scale data and ML systems in production.
Requirements
3+ years of engineering management experience leading Data / ML / Software engineering teams in production environments.
6+ years of experience building large-scale distributed systems in Data Engineering, ML Engineering, or Software Engineering roles.
Proven ownership of production-grade data or ML platforms, including delivery and adoption across R&D and Product stakeholders.
Hands-on experience building and operating high-scale distributed data systems (Spark, Storm, Flink) in production.
Strong experience with Java and Python in AWS cloud environments.
Advantages
Proven track record leading multi-disciplinary teams and driving measurable business impact through data/ML systems.
Experience building ML platforms, feature stores, or self-serve data infrastructure at scale.
Deep experience with modern ML/infra stack (PyTorch, TensorFlow, SageMaker, Kubernetes, Argo).
Experience with modern data lakehouse and analytics stack (Iceberg, Athena, ClickHouse, data catalogs, data quality frameworks).
Experience deploying LLM-based systems or AI-driven infrastructure in production environments.
