Software Engineering Team Lead - JB-764

We are looking for a Hands-On Software Engineering Team Lead to lead our platform team in building and scaling an end-to-end developer platform for Data Scientists. Our team owns the entire software development lifecycle, driving capabilities from DataOps (ingestion, cleaning, processing, indexing) to MLOps (experiment tracking, model registry, deployment pipelines). Core Responsibilities Technical Leadership & Engineering Culture (30–40%) ●Lead, mentor, and support a cross-functional software engineering team through sprint planning, technical roadmapping, and delivery. ●Establish and enforce best practices for code quality, testing, architectural patterns, and code reviews across backend, frontend, and infrastructure codebases. ●Collaborate with data scientists on the technical vision and system design for our internal Data & MLOps platform. Platform Development & Technical Execution (60–70% Hands-On) ●Write high-quality Python services and orchestrate complex data processing workflows. ●Build and maintain platform tools for data scientists, covering data preprocessing, indexing, experiment tracking, and model lifecycle management. ●Design and provision cloud environments on GCP. ●Contribute to and guide development on our web applications and internal tools built with React, Node.js, and TypeScript. ●Interface with large-scale data systems using data warehouses to enable fast querying and analytical processing. Required Qualifications & Technical Skills Technical Expertise ●Strong expertise in production Python development, asynchronous processing, and API design. ●Hands-on experience designing schemas, optimizing queries, and managing data in both relational databases (SQL) and NoSQL databases. ●Deep experience with Apache Airflow for workflow management and BigQuery (or Snowflake/Redshift) for large-scale data analytics. ●Hands-on experience with Docker and container orchestration platforms. ●Experience architecting solutions on the cloud using IaC. ●Hands-on knowledge of building and maintaining pipelines. ●Working knowledge of Node.js, React, and TypeScript. Preferred / Desirable Experience: ● Prior experience building developer-facing platforms, internal tools, or infrastructure specifically for Data Science, AI, or ML teams. ● Experience with open-source MLOps tools (e.g., MLflow, Kubeflow, Weights & Biases, Feature Stores). ● Deep understanding of database indexing, query optimization, and distributed data processing.