Senior Data Science Engineer

We are seeking aSenior Data Science Engineer to build reusable data-sharing adapters and governed access patterns that connect a cloud lakehouse to external analytics platforms while supporting reliable ML and LLM-enabled use cases. You will design scalable integrations, ensure secure tenant-scoped access, and deliver production-ready pipelines and tooling.

Responsibilities

• Design a dual-format lakehouse write layer that supports Delta and Iceberg metadata on one physical dataset
• Build and validate ingestion pipeline patterns from object storage to an analytical warehouse for structured operational data
• Implement change-data-capture patterns with Kafka for real-time and near-real-time movement into the lakehouse
• Develop dependency-aware bookkeeping and data lineage tracking patterns across data pipelines
• Ensure adapter code is modular, version-controlled, and reusable across new data source integrations
• Configure external table definitions for governed data products in Snowflake catalogs
• Validate zero-copy access from Snowflake to Iceberg and Delta tables without data movement
• Implement tenant-scoped access controls compatible with Snowflake governance metadata
• Implement and certify a Delta Sharing adapter for zero-copy sharing to Databricks consumers
• Configure Delta Sharing endpoint registration and sharing agreement management
• Validate Databricks read access via Delta Sharing for pandas, Spark, and other compatible consumers
• Test end-to-end freshness and sharing latency against agreed SLA targets
• Register external connector types and implement auditable RBAC and tenant-scoped authorization for all data-out paths
• Implement metering hooks compatible with the billing framework for governed data-out flows

Requirements

• 3+ years of data science and ML engineering experience using Python and SQL
• Strong leadership skills to drive technical decisions and delivery across integrations
• Proven project experience delivering production data pipelines and reusable adapters
• Advanced Python skills with clean, testable code and Git-based workflows
• Strong data engineering skills with pandas, data modeling, and warehouse/lakehouse concepts
• Hands-on ML skills with scikit-learn, experiment tracking, and model monitoring (drift, performance)
• Solid LLM and RAG skills including prompt engineering, embeddings, chunking strategies, and evaluation methods
• Strong software engineering skills in modular design, debugging, and basic system/API design with latency tradeoffs
• Working cloud fundamentals across GCP, AWS, or Azure for data and ML workloads
• Proficiency with AI-assisted development tools such as Claude Code, GitHub Copilot, or Cursor
• Upper-Intermediate English proficiency (B2)
• Strong communication skills to document integration patterns and align with stakeholders

Nice to have

• Google Cloud Platform experience with BigQuery, GCS, and lakehouse patterns
• Large Language Models (LLM) experience with API integration, rate limits, and cost management
• Vector database experience for RAG implementations, including indexing and retrieval tuning