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