Software Engineering Tech Lead - JB-764

We are looking for a Hands-On Software Engineering Tech 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.