Data Platform Tech Lead

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.

Nice to have

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

Requirements

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