Lead Data Science Engineer

We are seeking aLead Data Science Engineer to design and deliver reusable data sharing adapters and governed access patterns across a cloud lakehouse and external data platforms. You will guide high-impact architecture decisions, ensure reliable data and AI workflows, and help the team ship secure integrations.

Responsibilities

• Design a reusable lakehouse write layer with dual-format metadata to support multiple consumers
• Build and validate ingestion pipeline patterns from object storage to an analytical warehouse for structured operational data
• Implement change-data-capture patterns for real-time and near-real-time data movement into the lakehouse
• Develop dependency-aware bookkeeping and data lineage tracking patterns across pipelines
• Ensure adapter code is modular, version-controlled, tested, and reusable across new integrations
• Configure external table definitions for shared lakehouse data products in Snowflake catalogs
• Validate zero-copy read access from Snowflake to shared Iceberg and Delta tables without data movement
• Implement tenant-scoped access controls aligned with external catalog governance requirements
• Implement and certify a Delta Sharing adapter for live data sharing to Databricks consumers
• Configure Delta Sharing endpoints and manage sharing agreements for multiple tenants
• Validate consumer access via supported clients while meeting freshness and latency expectations
• Register connector types in a governed connector registry and enforce auditable RBAC on data-out paths
• Implement metering hooks compatible with governed billing requirements for external data flows

Requirements

• 5+ years of data science or ML engineering experience with production Python and pandas
• Experience building RAG applications using embeddings and retrieval pipelines
• Experience writing SQL for analytical data workflows and validation
• Strong technical leadership skills to drive architecture decisions and mentor peers
• Proven project delivery skills across multi-system data integration workstreams
• Solid software engineering skills in modular design, testing, debugging, and Git workflows
• Hands-on cloud platform skills with GCP, AWS, or Azure fundamentals
• Strong LLM fundamentals knowledge including tokenization, attention, context windows, and sampling
• Practical prompt engineering skills with structured outputs and few-shot techniques
• Robust evaluation and monitoring skills including drift, performance, and hallucination detection
• Strong communication and collaboration skills across engineering and data stakeholders
• Upper-Intermediate English proficiency (B2, Upper-Intermediate)
• Active experience using AI-assisted development tools such as Claude Code, GitHub Copilot, or Cursor

Nice to have

• Google Cloud Platform experience with BigQuery and object storage patterns
• Large Language Models (LLM) API integration experience including streaming, rate limits, and cost controls
• Vector database experience with indexing, chunking strategies, and retrieval tuning
• Experience optimizing LLM latency and cost using caching, batching, and model routing