Data Governance, Lineage & Privacy Engineering
As enterprises scale LLMs and Lakehouses, Data Governance is no longer bureaucratic paperwork—it is an automated engineering discipline. Master how to orchestrate metadata discovery with Collibra, Apache Atlas, and OpenLineage. Enforce automated data quality testing with Great Expectations, configure attribute-based access control (ABAC) in Snowflake/Databricks, and ensure compliance with India's Digital Personal Data Protection (DPDP) Act 2023 and GDPR.
🇮🇳 Indian Market Benchmark
AI Evaluation Casebook
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Core Track Highlights
Enterprise Data Governance & Lineage Architecture
Automated pipeline illustrating automated metadata crawling, DAG column lineage, data quality test suites, and dynamic DPDP privacy masking.
Automated Metadata Catalog
Continuous crawling across Snowflake, Postgres, and S3 using Apache Atlas and Collibra to index schemas and glossaries.
Column-Level Lineage
OpenLineage and dbt integration tracking data provenance from raw transaction logs all the way to executive BI dashboards.
Automated Quality Assertions
Great Expectations and Soda Core running CI/CD validation tests on completeness, schema drift, and data freshness.
Dynamic Privacy & DPDP Guardrails
Immuta and Privacera applying dynamic masking on Aadhaar, PAN, and PII in real-time queries.
Structured Phase-by-Phase Syllabus
Focus on build-by-doing milestones rather than passive video consumption.
Phase 1: Metadata Cataloging, Data Dictionaries & Business Glossaries
- Enterprise data cataloging principles and active metadata architectures
- Configuring Collibra / Apache Atlas crawlers for Snowflake, Postgres, and AWS Glue
- Building unified business glossaries, tagging taxonomies, and ownership matrices (RACI)
Phase 2: End-to-End Data Lineage & Automated Quality Pipelines
- Tracing column-level lineage using OpenLineage, dbt, and Marquez
- Implementing automated unit testing of data pipelines using Great Expectations
- Root-cause analysis (RCA) and anomaly alerts for upstream schema breakage
Phase 3: Data Privacy, DPDP 2023 Compliance & Access Control
- India Digital Personal Data Protection (DPDP) Act 2023 & GDPR legal requirements
- Implementing Dynamic Data Masking (DDM) and Row-Level Security (RLS)
- Configuring Attribute-Based Access Control (ABAC) with Immuta and Apache Ranger
Technical Interview Questions & Answers
Q1: What is the difference between Business Metadata, Technical Metadata, and Operational Metadata?
Technical Metadata describes physical schemas, data types, and table structures. Business Metadata adds human context, definitions, governance policies, and ownership tags. Operational Metadata records runtime telemetry such as query runtimes, row counts, freshness timestamps, and error logs.
Q2: How do you architect automated compliance for India's DPDP Act 2023 in a cloud data warehouse?
You establish automated classification crawlers to identify Personal Data (Aadhaar, PAN, phone numbers), enforce dynamic tokenization/masking so unprivileged analysts see hashed values, implement purpose-based consent tracking in the catalog, and maintain immutable query audit logs for regulatory inspections.
Frequently Asked Questions
Is Data Governance suitable for non-programmers?
While traditional data stewards focus on policies, modern Data Governance Engineers leverage SQL, Python, dbt, and APIs to automate compliance testing and lineage tracking.
Which certifications carry the most weight in India?
CDMP (Certified Data Management Professional by DAMA), Collibra Certified Ranger, and Snowflake SnowPro Advanced: Data Engineer.
Target Job Roles
Data Governance Engineer
Demand: HighData Stewardship & Lineage Lead
Demand: HighChief Data Office (CDO) Compliance Specialist
Demand: Moderate to HighRelated Career Tracks
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