Chief Data Architect

1 day ago

Agra, Uttar Pradesh, India beBeeDataArchitect Full time ₹ 48,00,000 - ₹ 72,00,000

We are seeking a seasoned data architect to lead the development of an enterprise-wide reporting solution. This is an exceptional opportunity for an expert in data architecture, engineering, and analytics platforms to join our team and shape the future of our business.

Key Responsibilities:
  • Data Modeling
    • Create a unified insurance data model encompassing policy, rating, endorsements, claims, billing, receivables/payables, and broker accounting—plus conformed dimensions (party, product, coverage, risk unit, geography, time).
  • Data Governance
    • Establish robust data contracts and versioned schemas with product teams; drive event standards for policy lifecycle, claims, and accounting events.
    • Design multi-tenant data isolation and security strategies, including workspace, schema, and row-level controls; enforce role-based access control/attribute-based access control, encryption, and key management aligned to industry standards.
  • Data Ingestion and Transformation
    • Build the ingestion and transformation layer: streaming (Apache Kafka/Azure Event Hubs) and batch (extract, transform, load/extract, transform, load) into a bronze/silver/gold (medallion) model; manage slowly changing dimensions, late-arriving data, and idempotency.
  • Semantic Layer
    • Stand up the semantic layer (e.g., Power BI/Fabric/Looker semantic models) with governed, reusable metrics.
  • Data Quality and Observability
    • Institute data quality checks, service-level agreements/service-level objectives, lineage, and monitoring (contracts, schema drift, nullability, referential integrity, reconciliation to source).
  • Governance & Compliance
    • Own metadata management, lineage, catalog, personally identifiable information handling, retention, audit trails; champion data stewardship processes.
  • Performance, Reliability & Cost Optimization
    • Drive performance, reliability, and cost optimization: partitioning/clustering, query acceleration, workload management, caching, workload isolation, and cost guardrails.
  • Tooling Roadmap
    • Publish the tooling roadmap: Azure-native and complementary tools (Azure Data Lake Storage Gen2/Fabric or Databricks + Delta, Synapse/Fabric pipelines, Azure SQL/PostgreSQL, Azure Purview, Azure Key Vault, Power BI).
  • Enablement
    • Enable technical enablement: reference implementations, style guides, model playbooks; run data design reviews with product squads; mentor data engineers/analysts.
  • Release Management
    • Manage release cycles for data: continuous integration/continuous deployment for pipelines/models, automated tests (unit/data tests), backward-compatible schema versioning, and environment promotions.

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