Data Tech Lead

The Opportunity

We are looking for a Technical Lead to operate at the high-stakes intersection of Data Engineering, ML Systems, and Cloud Architecture. In this pivotal role, you will define the patterns, architectural guardrails, and core platform capabilities that empower engineering teams to deliver AI-native, ultra-low-latency products. Your mission is to ensure our systems remain scalable, resilient, and cost-efficient while maintaining the highest standards of reliability.

What You’ll Do

• End-to-End Data Ownership: Architect and operate complex batch and streaming pipelines (utilizing Lakehouse, CDC, and real-time ingestion) with a focus on data contracts, lineage, and quality.
• Empower AI/ML Production: Design the underlying architecture for LLM and ML data pipelines—encompassing embeddings, vector search, RAG frameworks, and real-time inference.
• Architectural Standards: Establish "contract-first" APIs and event schemas. Lead design reviews to set SLOs for freshness, latency, and cost-effectiveness.
• Distributed Systems Design: Apply deep expertise in event-driven and microservices patterns, including idempotency, backfills, retries, and DLQ strategies for safe scaling.
• Pragmatic Modernization: Guide the strategic adoption of modern tools like Databricks, Kafka, Dagster, and Kubernetes within cloud-native environments.
• Strategic Mentorship: Lead through influence by partnering with Product and ML teams to translate high-level goals into concrete architectures and measurable technical outcomes.

What You’ll Bring

• Technical Leadership: 5+ years of experience as a Tech Lead or Data Architect in AI-intensive environments, with approximately 10+ years of total engineering experience.
• Distributed Systems Mastery: Deep understanding of microservices, backpressure management, and event-driven architecture.
• Data Infrastructure Expertise: Proven track record in building Lakehouse architectures and governing large-scale data flows.
• Full-Stack Hands-on Ability: High proficiency with tools such as ClickHouse, Databricks, Airflow/Dagster, Kafka, Kubernetes, and Terraform across major cloud providers (AWS, GCP, Azure).