Title:  Sr Technical Lead-Data Engg

Area(s) of responsibility

Required Skillset:

• Hands-on experience with MS Purview
• Strong attention to detail and analytical mindset
• Proven ability to translate business requirements into technical configurations
• Knowledge on Ataccama and Databricks Unity Catalog

Key Responsibilities (Must-Have):

• Set architecture and technical standards for metadata, policies, and access governance across Azure
• Design and deploy metadata scanning, lineage, and classification pipelines using Microsoft Purview.
• Implement and manage Catalog to ensure centralized governance, RBAC, and data lineage. 
• Integrate Purview with Databricks for unified data discovery and compliance. Collaborate with security, compliance, and data engineering teams to ensure governance aligns seamlessly with business needs. Broad adoption of Microsoft Purview integrations across your enterprise
• Development of metadata-driven governance practices, boosting compliance and operational efficiency
• Mentor engineers; oversee code reviews, design docs, and best practices

Reference Data Enablement

• Ingest, standardize, and govern regional and global reference data sets
• Implement business-defined mappings (e.g., industry classification, management hierarchies) to support consistent reporting

Data Quality Enablement

• Configure and manage data rules for profiling, issue tracking, enrichment, cleansing, freshness checks, anomaly detection, and scanner coverage

Collaboration & Governance

• Partner with regional Heads of Data Governance and to align implementations with global frameworks
• Support governance adoption and consistent rule application across business units

Expanded Responsibilities (Nice-to-Have):

Lineage & Impact Analysis

• Enable visualization of data quality lineage and perform impact analysis across domains

Change Management & Enablement

• Assist in creating training curriculum on data quality, data governance, data literacy and documentation for data stewards and business users
• Promote data quality literacy and best practices across regions