DE&A - Core - Project Management - Project Management

Job description

**Role Overview** 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. **Key Responsibilities** **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. **Required Qualifications** - 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. **Preferred Qualifications** - 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).