Data Management Specialist

Position Summary

The MDM Technical Specialist is the platform owner for company's Tamr MDM implementation. This role is responsible for the technical design, configuration, integration, and ongoing operation of Tamr across all 5 master data domains: Product/Model, Asset/Standard, Organizations, Locations, and Contacts. Unlike traditional MDM platforms that require manual rule writing, Tamr uses machine learning for entity resolution — making this role a blend of data engineering, ML operations, and platform administration. The MDM Technical Specialist works in close partnership with the Principal Data Engineer (lakehouse integration), the Senior Data Governance & MDM Analyst (governance and policy), and the Master Data Steward (domain operations).

• Core Responsibilities
• Tamr Platform Implementation & Configuration
• Lead the technical implementation of Tamr for each domain: project creation, schema mapping, attribute configuration, and source system connection.
• Configure and tune ML matching models per domain; evaluate match quality and iterate on model inputs to improve precision and recall.
• Manage golden record configuration: define survivorship rules, attribute priority rankings, and confidence scoring thresholds for each domain.
• Administer Tamr platform: user access, project governance, version management, and environment configuration (dev/test/prod).

• Source System Integration
• Build and maintain ingestion pipelines from source systems (ERP, CRM, Field Service) into Tamr projects using Tamr's REST API and Python SDK.
• Design schema normalization and attribute mapping for each source system contributing to a given domain.
• Implement change data capture or scheduled sync patterns to keep Tamr domain data current.

• Golden Record Output & Lakehouse Integration
• Build and maintain golden record output pipelines from Tamr to the Azure Data Lakehouse (Delta Lake/Gold layer).
• Partner with the Principal Data Engineer to ensure golden record schema, confidence scores, and lineage are correctly represented in the lakehouse.
• Ensure golden record publication SLAs are met; monitor pipeline freshness and alert on stale or failed outputs.
• ML Model Operations
• Monitor Tamr ML model performance over time; identify degradation and trigger retraining or model tuning cycles.
• Implement active learning workflows: route low-confidence matches to the Master Data Steward for review; feed steward decisions back into model training.
• Document model configurations, training data characteristics, and performance baselines for each domain.
• Domain Onboarding & Expansion
• Lead the technical onboarding of each new domain; coordinate with source system teams on data extraction and schema alignment.
• Define and maintain domain-specific data profiles and completeness benchmarks.
• Manage multi-domain relationship configuration in Tamr (e.g., Contact → Organization, Organization → Location).
• Platform Operations & Support
• Provide production support for the Tamr platform; respond to and resolve platform incidents within defined SLAs.
• Manage Tamr upgrades, patches, and configuration changes through the change management process.
• Maintain technical documentation: architecture diagrams, integration specs, and runbooks for all Tamr operations.

• Competency Expectations
• Competency Area
• Expectation at This Level
• Scope
• Full technical ownership of the Tamr MDM platform across all 5 domains and all integration points.
• Independence
• Fully autonomous on Tamr platform decisions; escalates only for source system access or infrastructure constraints.
• Technical Depth
• Expert in Tamr platform administration, Python SDK, and REST API integration; strong data engineering foundations; ML operations awareness.
• Mentorship
• Trains Master Data Stewards on Tamr UI workflows; guides the governance team on platform capabilities and limitations.
• Business Acumen
• Understands the business impact of golden record quality; bridges technical platform decisions to data trust outcomes.

• Required Qualifications
• 5+ years of data engineering or MDM technical experience; 2+ years working with an enterprise MDM platform in production.
• Hands-on experience with Tamr (strongly preferred) or a comparable ML-driven MDM platform (Reltio, Informatica MDM Multidomain, or equivalent).
• Proficiency in Python for data pipeline development; experience with REST API integration and JSON data.
• Experience building and maintaining data integration pipelines between source systems and a cloud data platform.
• Understanding of entity resolution, deduplication, and record linkage concepts.
• Strong documentation skills; able to produce architecture diagrams, runbooks, and integration specs.

• Preferred Qualifications
• Tamr platform certification or formal Tamr training.
• Experience integrating MDM platforms with Azure Data Lakehouse or Databricks
• Familiarity with active learning and human-in-the-loop ML workflows.
• Background in manufacturing, distribution, or field services industries with complex product and asset data.
• Experience with multi-domain MDM relationship configuration (e.g., party-address-contact hierarchies).

• Success Metrics
• All 5 domains onboarded to Tamr with production-grade ingestion pipelines operational.
• Match precision and recall within defined thresholds for each domain (documented baseline established).
• Golden record output pipeline live and publishing to the lakehouse on defined SLA for all active domains.
• ML model retraining process documented and operational; active learning workflow in place with Master Data Steward.
• Platform uptime at 99.5%+ for production Tamr environment; zero critical incidents unresolved beyond SLA.