[Hiring] Cloud Data Engineering - Snowflake Data Engineering @Zensar

Role Description

The Senior Analytics Engineer (Data Products) will partner with the SVP of Data & Product Analytics, focusing on the following:

• Engagement model: No people-management responsibilities.
• Primary outputs:
  • Cross-product aggregated datasets
  • A semantic layer for metrics and curated datasets
  • Clear dataset documentation and definitions
• Ways of working: Async-first with written specs, review cycles, and lightweight live syncs during overlap hours.
• Access and tooling: Work performed in company-managed systems (e.g., Databricks) subject to onboarding and access approvals.

What You'll Do

• Partner on Unity Catalog operations:
  • Organize catalogs and schemas, tighten naming conventions, and implement permission patterns.
  • Fix access or discoverability issues and document patterns.
• Partner to deliver the silver and gold layer:
  • Design transformation logic, define table and metric definitions, review outputs, and validate results.
  • Contribute directly (SQL, notebooks, documentation).
• Build cross-product aggregated datasets:
  • Implement canonical datasets that join and roll up measures and dimensions.
  • Optimize for consistent definitions and performance.
• Operate datasets like products:
  • Version key tables and set clear expectations for data freshness and completeness.
  • Communicate changes before they affect downstream workflows.
• Build and publish the semantic layer:
  • Implement metric definitions and a business glossary.
  • Publish examples for self-service in Databricks.
• Partner on data contracts and quality checks:
  • Work on contracts, schema checks, and lineage to ensure data trustworthiness.
  • Define quality standards and support triage for issues.
• Support self-service and answer questions:
  • Publish examples and documentation for safe querying.
  • Answer questions and unblock teams.
• Keep documentation in sync with production:
  • Maintain dataset definitions and metric standards as tables evolve.

Qualifications

• 6 to 10 years in data engineering, analytics engineering, or a closely related role.
• Databricks experience, including hands-on work with Unity Catalog, Delta Lake, and SQL or notebook-based development.
• Proficiency in Python and SQL at an engineering level.
• Solid understanding of medallion architecture (bronze to silver to gold).
• Experience building and supporting semantic layers, data catalogs, or self-service data products in production.
• Track record of building shared, cross-domain datasets.
• Strong stakeholder management skills.
• Comfortable with modern engineering workflows: Git-based version control, code review, and testing.
• Strong written communication skills.

Nice to Have

• Experience with Databricks Genie or AI-BI features.
• Familiarity with MCP (Model Context Protocol), LLM tool calling, or AI agent patterns.
• Background in financial services, insurance, or reinsurance data.

How You'll Work

You will collaborate closely with the SVP of Data & Product Analytics in an async-first model. Strong written communication and proactive flagging of blockers are essential.

Success in the First 60 to 90 Days Looks Like

• Improved Unity Catalog navigation and access for high-value areas.
• First set of silver and gold tables in production, validated with actuarial users.
• Live semantic layer with core metrics defined and curated datasets published.
• Data contracts and basic validation checks running for new or changed datasets.
• At least one cross-product dataset shipped and used by multiple downstream consumers.

Hours

The team runs a data workstream sync at 8:45am EST on Monday, Wednesday, and Friday. Availability during this time is preferred, with self-directed work outside of these hours.