Job Description

You will own the end-to-end design and delivery of our Master Data Management (MDM) framework, Data Quality & Governance (DQG) pipelines, and enterprise Data Catalog. Working closely with product, analytics, and platform engineering teams, you will transform fragmented data assets into trusted, AI-ready data products — enabling everything from self-serve analytics to real-time ML inference. This is a senior individual contributor role with a clear path to staff/principal, and early exposure to AI-augmented data management tooling.

1. Master Data Management (MDM)

• Design and implement a scalable MDM architecture covering customer, product, and entity master domains.
• Build and maintain golden record pipelines using entity resolution, probabilistic matching, and survivorship rules.
• Leverage Neo4j graph models to represent complex entity relationships and hierarchies that RDBMS cannot capture.
• Drive cross-functional data stewardship workflows — from source profiling to master record certification.

2. Data Quality & Governance (DQG)

• Establish and operationalise a DQ framework: define critical data elements (CDEs), quality dimensions, and SLA thresholds.
• Build automated DQ checks (completeness, uniqueness, validity, timeliness) integrated into CI/CD pipelines.
• Instrument data observability tooling (Monte Carlo, Soda Core, or equivalent) to detect and alert on anomalies in real time.
• Develop and maintain data governance policies in alignment with GDPR, CCPA, and ISO 8000 standards.
• Produce executive-facing data quality scorecards and lineage dashboards.

3. Data Catalog & Metadata Management

• Own the enterprise data catalog (Collibra, Alation, or open-source equivalent), including taxonomy, glossary, and ownership models.
• Implement active metadata strategies: auto-tagging, lineage capture, and semantic enrichment using LLMs.
• Drive catalog adoption across engineering, analytics, and business teams through self-serve onboarding.
• Integrate catalog metadata with downstream AI feature stores and ML pipelines to ensure feature provenance and reusability.

4. AI-Augmented Data Management

• Apply ML and LLM techniques to automate DQ rule generation, anomaly detection, and metadata enrichment.
• Build AI-powered entity resolution models (embeddings + graph algorithms) to replace rule-based matching.
• Collaborate with data science teams to deliver clean, governed, AI-ready datasets for model training and inference.
• Evaluate and pilot emerging AI data tools (e.g. DataHub AI, OpenMetadata, custom RAG pipelines over catalog metadata).
• Contribute to the internal AI data platform roadmap — helping define the standards for how AI models consume governed data.

5. Platform Engineering & Delivery

• Design reusable data pipeline patterns on cloud-native stacks (AWS/GCP/Azure) using Spark, dbt, Airflow, or equivalent.
• Mentor junior engineers; conduct design reviews and enforce engineering best practices.
• Partner with data product owners to define and deliver certified data products on a data mesh architecture.

• 8+ years in data engineering with demonstrable depth in at least two of: MDM, DQG, or Data Catalog implementation.
• Hands-on experience with at least one enterprise MDM platform (Informatica MDM, Reltio, Semarchy, or custom-built).
• Proficiency in SQL, Python, and Spark for large-scale data processing.
• Strong understanding of data governance frameworks (DAMA-DMBOK, DCAM, or equivalent).
• Experience with a Data Catalog platform at production scale (Collibra, Alation, Atlan, DataHub, or OpenMetadata).
• Working knowledge of graph data concepts — Neo4j Cypher experience is a strong advantage.
• Hands-on exposure to ML/AI tooling: model training, feature engineering, or LLM-based automation.
• Experience operating in cloud environments (AWS Glue, GCP Dataplex, Azure Purview, or equivalent).
• Strong communication skills — able to translate data governance concepts for non-technical stakeholders.

• Neo4j Certified Professional or equivalent graph database certification.
• Experience with data mesh principles and building certified data products.
• Familiarity with vector databases and embedding-based search (Pinecone, Weaviate, or pgvector).
• Contributions to open-source data governance or catalog projects.
• Background in financial services, healthcare, or other regulated industries with stringent data compliance requirements.
• Experience with real-time streaming data quality (Kafka + Great Expectations / Soda).

MDM,DQG, Data Catalog person with strong knowledge on AI

MDM,DQG, Data Catalog person with strong knowledge on AI

At Zensar, we’re “experience-led everything” We are committed to conceptualizing, designing, engineering, marketing, and managing digital solutions and experiences for over 130 leading enterprises. We are a company driven by a bold purpose: Together, we shape experiences for better futures Whether for our clients, our people, or the world around us, this belief powers everything we do. At the heart of our culture is ONE with Client - a set of four core values that reflect who we are and how we work: One Zensar, Nurturing, Empowering, and Client Focus

Part of the $4.8 billion RPG Group, we’re a community of 10,000+ innovators across 30+ global locations, including Milpitas, Seattle, Princeton, Cape Town, London, Zurich, Singapore, and Mexico City. Explore Life at Zensar and join us to Grow. Own. Achieve. Learn. to be the best version of yourself.

We believe the best work happens when individuality is celebrated, growth is encouraged, and well-being is prioritized. We are an equal employment opportunity (EEO) and affirmative action employer, committed to creating an inclusive workplace. All qualified applicants will be considered without regard to race, creed, color, ancestry, religion, sex, national origin, citizenship, age, sexual orientation, gender identity, disability, marital status, family medical leave status, or protected veteran status.