Unified Data Platform Architect

1 day ago

Noida, Uttar Pradesh, India beBeeDataIntegration Full time ₹ 16,00,000 - ₹ 22,40,000
Job Description

As a Data Integration Architect, you will be responsible for designing and operating pipelines that unify CRM, billing, support, and SaaS app data into our Azure-native data fabric. This role sits at the intersection of data engineering and platform architecture — perfect for someone who enjoys solving complex data puzzles and wants to grow into a Lead Data Architect role.

The Key Responsibilities:

  • Build ingestion pipelines (CDC, batch, webhook, file-based) from CRMs (Salesforce, Dynamics, HubSpot), billing systems (Stripe, Zuora), and support tools (Zendesk, JSM) into Azure Fabric Bronze/Silver layers.
  • Curate standardized models in Fabric Silver → Gold using Canonical RevAi Schema (CRS), including SCD2 handling, PII tagging, and business-rule conformance.
  • Operate low-latency egress pipelines from Fabric → Postgres (staging → ext_* → app consumption), ensuring subsecond query performance for AI agents.
  • Implement tenant isolation via RLS in Postgres and Purview policies in Fabric, ensuring strict multi-tenancy and compliance with residency rules.
  • Own observability & SLAs: freshness checks, rowcount anomaly detection, and DQ tests across Bronze/Silver/Gold.
  • Contribute to FinOps dashboards by instrumenting pipeline cost metrics, cache hit rates, and egress efficiency.
  • Collaborate with AI engineers to ensure pgvector embeddings and agent run data integrate seamlessly into the operational data store.
  • Write runbooks for DR/retention/erasure, and actively participate in quarterly pen-test preparations.

Requirements

Must-Have Skills:

  • 2–4 years in data engineering / integration roles.
  • Hands-on with Azure Data Factory, Azure Fabric (OneLake/Lakehouse), or equivalent (Databricks/Snowflake acceptable).
  • Strong SQL and PostgreSQL experience (MERGE, staging, RLS, partitioning).
  • Experience with CDC, batch ingestion, or event streaming (Airflow, dbt, Kafka, or Fabric pipelines).
  • Comfort with data governance concepts (Purview, PII tagging, masked views, data lineage).

Nice-to-Have Skills:

  • Knowledge of pgvector, embeddings, or AI data models.
  • Prior exposure to multi-tenant SaaS architectures.
  • Experience with observability tooling (freshness checks, data quality gates, anomaly detection).
  • Familiarity with FinOps or cost-optimization in data pipelines.

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