Elevate Your Career as a Senior Data Architect

2 days ago

Bareilly, Uttar Pradesh, India beBeeData Full time ₹ 45,00,000 - ₹ 60,00,000
Job Title:

Senior Data Architect

Design and implement end-to-end data architectures for cross-product reporting solutions, spanning lakehouse/warehouse, streaming/batch pipelines, semantic layers, and business intelligence.

Key Responsibilities:
  1. Own the end-to-end data architecture for a cross-product reporting solution, incorporating robust data isolation and security measures.
  2. Establish canonical insurance data models, encompassing policy lifecycle, coverages/endorsements, claims, premiums, reserves, bordereaux, and broker accounting.
  3. Define data contracts and schemas with product teams, driving event standards for policy lifecycle, claims, and accounting events.
  4. Design multi-tenant data isolation strategies, enforcing role-based access control, attribute-based access control, encryption, and key management aligned to industry standards.
  5. Develop the ingestion and transformation layer, incorporating streaming (Kafka/Event Hubs) and batch (ELT/ETL) processes into a bronze/silver/gold model, managing slow-changing dimensions, late-arriving data, and idempotency.
  6. Establish the semantic layer, utilizing governed, reusable metrics through platforms like Power BI/Fabric/Looker.
  7. Institute data quality and observability, implementing checks, SLAs/SLOs, lineage, and monitoring across contracts, schema drift, nullability, referential integrity, and reconciliation to source.
  8. Ensure governance & compliance, managing metadata, lineage, catalog, PII handling, retention, audit trails, and championing Purview or equivalent data stewardship processes.
  9. Drive performance, reliability & cost optimization, leveraging partitioning/clustering, query acceleration, workload management, caching, workload isolation, and cost guardrails.
  10. Publish the tooling roadmap, highlighting Azure-native and complementary tools (ADLS Gen2/Fabric or Databricks + Delta, Synapse/Fabric pipelines, Azure SQL/PostgreSQL, Purview, Key Vault, Power BI).
  11. Enablement: provide reference implementations, style guides, model playbooks; run data design reviews with product squads; mentor data engineers/analysts.
  12. Release management for data: implement CI/CD for pipelines/models, automated tests (unit/data tests), backward-compatible schema versioning, and environment promotions.
  13. Collaborate with Product, Engineering, and Implementation to prioritize analytics use cases and deliver roadmaps tied to customer outcomes.

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