Software Engineering Team Lead

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%)

• People & Delivery Leadership: Lead, mentor, and support a cross-functional

software engineering team through sprint planning, technical roadmapping, and delivery.

• Code Quality & Standards: Establish and enforce best practices for code quality,

testing, architectural patterns, and code reviews across backend, frontend, and infrastructure codebases.

• Architecture & System Design: 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)

• Backend, Data Pipelines, & Databases: Write high-quality Python services and

orchestrate complex data processing workflows.

• MLOps & DataOps Capabilities: Build and maintain platform tools for data

scientists, covering data preprocessing, indexing, experiment tracking, and model lifecycle management.

• Cloud Infrastructure & Platform Engineering: Design and provision cloud

environments on GCP.

• Full-Stack Tooling: Contribute to and guide development on our web applications

and internal tools built with React, Node.js, and TypeScript.

• Data Analytics & Warehousing: Interface with large-scale data systems using data

warehouses to enable fast querying and analytical processing.

Required Qualifications & Technical Skills Technical Expertise

• Python expertise: Strong expertise in production Python development,

asynchronous processing, and API design.

• Database Experience: Hands-on experience designing schemas, optimizing

queries, and managing data in both relational databases (SQL) and NoSQL databases.

• Data Orchestration & Warehousing: Deep experience with Apache Airflow for

workflow management and BigQuery (or Snowflake/Redshift) for large-scale data analytics.

• Containerization: Hands-on experience with Docker and container orchestration

platforms.

• Cloud & Infrastructure as Code: Experience architecting solutions on the cloud

using IaC.

• CI/CD Automation: Hands-on knowledge of building and maintaining pipelines.
• Full-Stack Proficiency: 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.