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).

MDMDQG Data Catalog person with strong knowledge on AI

MDMDQG Data Catalog person with strong knowledge on AI