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.
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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.
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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.
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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.
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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.
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- 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.
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- 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).
