Data Platform Tech Lead

Why Join Us?

We're looking for a Tech Lead who is more than a great data engineer - someone who brings equal depth in backend software engineering and broad fluency across data technologies, databases, and large-scale distributed systems. You'll own the architecture and hands-on development of our data platform while driving engineering excellence across the team.

Key Responsibilities

Architect and evolve our Data Warehouse, Data Lake, and ETL/ELT pipelines across multiple products and business domains

Design and build enterprise-grade backend services and APIs that expose data platform capabilities at scale

Own technical decisions on scalability, reliability, observability, and data quality

Lead cross-functional initiatives with R&D, product, analysts, and business stakeholders

Mentor data engineers and developers through design reviews, technical leadership, and engineering best practices

Drive adoption of modern data technologies, AI-first practices, and automation

Qualifications

6+ years in Data Engineering, including production-scale platform design and operation using Spark

2+ years as a Tech Lead or senior engineer leading technical initiatives

Hands-on backend development experience - REST/gRPC APIs, microservices, service design patterns - at enterprise scale

Broad database fluency: relational, analytical, NoSQL, columnar, and streaming stores

Advanced SQL and strong Python; proven experience building and optimizing large-scale ETL/ELT pipelines

Experience with cloud-based data platforms and AI-driven data workflows

Practical proficiency with AI agents and tools - building, integrating, or orchestrating LLM-powered workflows in production environments

Strong communication skills with technical and non-technical stakeholders

Self-directed and forward-thinking - you bring a clear point of view, drive initiatives without being told, and see around corners on where the platform needs to go next

Nice to have

Azure data services, Databricks or EventHub

Streaming and real-time data processing architectures

Background in cyber security, SaaS, or large-scale enterprise environments

Data governance, observability, or self-service platform experience

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