Overview

We are seeking a hands-on Senior MDM Developer to take a custom, warehouse-native Customer MDM solution from design and partially implemented SQL to a reliable running master-data hub. The solution does not use a commercial MDM platform. Its party model, source crosswalks, matching and scoring logic, survivorship rules, reversible merge mechanics, and golden-record pipelines are implemented directly using Snowflake, dbt, SQL, and PostgreSQL. The role focuses on Customer and Individual/Consumer mastering across D&B, SAP, MTS, Sideline, DAL, and other source systems. The successful candidate will challenge and refine the existing design, write production code, measure match quality against labelled data, and iteratively execute the Load → Match → Merge → Review → Refine lifecycle until the resulting golden records are trustworthy, explainable, and traceable.

Important Role Context

• This is a build role for a custom MDM hub—not a role configuring Informatica MDM, Reltio, Profisee, SAP MDG, Ataccama, or another packaged MDM product.
• Commercial MDM platform experience is not required and does not substitute for strong data modeling, record-linkage, SQL, and engineering skills.
• The developer will inspect the data, challenge assumptions, identify when documented processing is not actually running, and support conclusions with measured evidence.

Key Responsibilities

• MDM Solution and Architecture Review
  • Review the latest MDM solution design, including the architecture, Customer and Individual/Consumer data models, source systems, integration patterns, loading approach, and processing logic.
  • Validate that the architecture supports initial and incremental loads, data quality controls, match and merge processing, exception handling, stewardship, lineage, auditability, restartability, and future scale.
  • Identify technical gaps, dependencies, implementation risks, and practical improvement opportunities.
• Customer and Consumer Data Modeling
  • Validate the MDM data model for organization, customer, individual, consumer, household, account, affiliation, and hierarchy use cases.
  • Assess support for identifiers, names, addresses, emails, phone numbers, classifications, roles, relationships, statuses, lifecycle dates, consent, privacy preferences, source lineage, and history.
  • Identify missing attributes, entities, relationships, reference data, and metadata; propose and implement required logical and physical model changes.
  • Ensure the model retains source-record cross-references, attribute-level provenance, match evidence, survivorship decisions, and golden-record history.
  • Implement a party model in which organizations and people share a governed party root and “customer” is represented as a role, with appropriate subtypes, identifiers, relationships, lineage, and merge events.
  • Model multiple hierarchies as governed edges—such as legal, operational, affiliation, and commercial rollups—so only authorized relationship types drive aggregation.
• Source-System Analysis and Mapping
  • Profile and analyze data from Dun & Bradstreet (D&B), SAP, MTS, Sideline, DAL, and other relevant source systems.
  • Work with source-system SMEs to understand business definitions, ownership, quality, refresh frequency, identifiers, and downstream usage.
  • Identify duplicate, incomplete, inconsistent, conflicting, stale, and invalid data and quantify their effect on mastering outcomes.
  • Define source-to-MDM mappings covering direct mappings, transformations, standardization, reference translations, default and derived values, mandatory fields, data-quality validations, and rejection rules.
  • Determine which MTS information is relevant to MDM and define how it should be incorporated into the model, match process, and golden record.
• Data Loading and Snowflake Development
  • Design and prepare the initial data-loading process and support subsequent incremental and restartable loads.
  • Review, develop, and optimize existing Snowflake SQL, scripts, stored procedures, views, queries, loading sequences, dependencies, and processing logic.
  • Implement a controlled processing sequence for ingestion, staging, profiling, cleansing, standardization, validation, candidate generation, matching, scoring, merging, survivorship, golden-record creation, exception review, and publication.
  • Build reconciliation controls, record-count checks, audit logging, error handling, recovery procedures, and operational monitoring.
• Matching, Scoring, and Entity Resolution
  • Analyze and validate existing matching and scoring logic; identify false-positive and false-negative risks.
  • Define and refine deterministic, probabilistic, fuzzy, and composite rules for Customer and Individual/Consumer matching.
  • Use relevant identifiers and attributes, including source customer IDs, D-U-N-S Numbers, names, standardized addresses, emails, phone numbers, tax or registration identifiers, geography, affiliations, and account relationships.
  • Define blocking and candidate-selection strategies and calibrate thresholds for automatic match, possible match/manual review, and non-match outcomes.
  • Generate transparent match scores, reason codes, evidence, and rule versions to support stewardship and audit review.
  • Tune blocking recall, match precision, match recall, and thresholds against labelled links or other approved ground truth, reporting results separately for relevant consuming uses.
  • Apply negative evidence and veto rules where shared or conflicting contact points would otherwise create unsafe merges.
  • Resolve people at consumer scale where reference anchors may not exist and shared email addresses or phone numbers can connect multiple accounts.
  • Use stricter person-match thresholds where over-merging could expose one consumer’s data to another, treating privacy risk differently from an organization-matching error.
• Survivorship, Merge, and Golden-Record Creation
  • Define attribute-level survivorship rules based on source trust, quality, completeness, verification status, recency, consistency, and business-defined precedence.
  • Progress the merge, cross-reference, and golden-record creation process while preserving source traceability and valid source detail.
  • Support merge, unmerge, manual override, exception review, and controlled reprocessing requirements.
  • Maintain an auditable explanation of why each golden value was selected and ensure matching and survivorship rules are version controlled.
  • Implement crosswalk-based, reversible merges that never mutate original source records, record merge events, support replay/unmerge, and provide defined redirects for merged-away identifiers.
  • Implement survivorship as governed, data-driven rules where practical, with firing telemetry, effective dating, and regression gates.
  • Treat consent and communication preferences as source-specific governed assertions; do not combine them through ordinary survivorship logic.
• Iterative Execution and Validation
  • Execute the MDM lifecycle iteratively: Load → Standardize → Match → Merge → Review → Refine → Validate.
  • Analyze each cycle and refine mappings, standardization logic, thresholds, matching rules, and survivorship rules based on evidence.
  • Validate master and golden records for accuracy, completeness, uniqueness, consistency, usability, and source-to-golden traceability.
  • Investigate duplicate clusters, incorrect merges, missed matches, conflicting values, incomplete records, and other anomalies.
  • Partner with business data stewards to resolve ambiguous matches and execute regression testing after rule or model changes.
• Documentation and Collaboration
  • Document source-to-MDM mappings, data-model changes, cleansing rules, data-quality controls, match/scoring rules, thresholds, reason codes, survivorship rules, processing sequences, issues, and decisions.
  • Maintain reconciliation results, validation evidence, exception logs, and technical runbooks.
  • Collaborate with data architects, business analysts, data stewards, governance teams, source-system owners, QA teams, and downstream consumers.
  • Translate business policies and data definitions into maintainable, testable, and explainable MDM logic.

Required Skills

• Bachelor’s degree in Computer Science, Information Systems, Engineering, Data Management, or a related discipline.
• 7–12 years of experience in data engineering, data integration, or enterprise data management, including substantial hands-on responsibility for customer mastering or identity resolution.
• Strong party and entity-relationship modeling skills: ability to reason from first principles about entities, roles, identifiers, relationships, identity, lineage, and the consequences of different modeling choices.
• Hands-on probabilistic record-linkage experience covering blocking, candidate generation, scoring, thresholds, negative evidence, vetoes, and precision/recall trade-offs.
• Experience tuning a matcher against labelled data or defensible ground truth and explaining the impact and cost of rule changes.
• Experience with privacy-aware person matching at consumer scale, including ambiguous or heavily shared contact points and the asymmetric risk of over-merging individuals.
• Strong understanding of attribute-level survivorship, source provenance, mirrored-source risks, consent handling, and rule governance.
• Experience designing or implementing reversible, crosswalk-based merge/unmerge processing with immutable source records and stable identifier resolution.
• Advanced SQL skills for large datasets, including window functions, recursive CTEs, closure tables, set-based MERGE patterns, query tuning, and warehouse-scale performance reasoning.
• Hands-on dbt or equivalent transformation-as-code experience, including tests, lineage, modular models, documentation, source freshness, and version control.
• Experience reconciling an internal customer estate to authoritative external reference data and deduplicating reference sources before they influence mastered identities.
• Strong ability to profile real data, validate what processing actually occurred, communicate discrepancies directly, and document evidence-based decisions.

Preferred Skills

• Hands-on Snowflake and PostgreSQL experience in a warehouse-native data hub or large-scale analytical environment.
• Experience integrating and reconciling D&B data and D-U-N-S identifiers with an internal customer estate.
• Knowledge of SAP customer-master structures and extraction patterns, plus experience with diverse operational source systems.
• Python experience for data profiling, transformation libraries, match evaluation, and repeatable quality analysis.
• Graph or network modeling experience for identity resolution, connected components, and hierarchy traversal.
• Experience applying embeddings or vector similarity to entity resolution or attribute normalization with appropriate validation controls.
• Experience designing data-stewardship workflows, exception queues, human-in-the-loop review, and feedback capture.
• Exposure to US education, K–12, youth-sports, institutional, or membership-oriented customer data is valuable.

Qualifications

• Reviewed solution design with a documented architecture, data-model, SQL/process, and implementation gap assessment.
• Validated party model for organizations and individuals, including governed relationship and hierarchy edges.
• Source-data profiling and quality assessment for D&B, SAP, MTS, Sideline, DAL, and other approved sources.
• Approved source-to-MDM mappings, reference-data reconciliation rules, and MTS incorporation approach.
• Production-ready initial and incremental loading models using Snowflake/dbt/PostgreSQL with testing, monitoring, reconciliation, and recovery controls.
• Measured match baseline and improved blocking recall, precision, recall, thresholds, negative evidence, and veto rules against approved ground truth.
• Versioned, attribute-level survivorship rules with provenance controls, rule-firing telemetry, and regression tests.
• Reversible crosswalk-based merge/unmerge process and golden-record pipeline with lineage and merged-ID resolution.
• Golden-record validation results, exception analysis, issue/decision register, and prioritized remediation plan.
• Technical documentation and operational runbook for repeatable Load → Match → Merge → Review → Refine execution.

Success Measures

• Improved and measured blocking recall, match precision, and match recall against labelled data or approved ground truth.
• Privacy-safe person matching with explicit controls for shared contact points and over-merge risk.
• Accurate, complete, unique, explainable, and traceable golden customer records.
• Reversible merges with immutable source records, complete crosswalks, and reliable merged-identifier resolution.
• Complete attribute-level provenance and defensible golden-value selection.
• Reconciled counts and tests across source, staging, match, merge, golden, and publish stages.
• Repeatable, restartable, monitored, version-controlled, and auditable MDM processing.
• Business and data-steward approval of matching, survivorship, hierarchy, and golden-record outcomes.

Core Technology and Domain Keywords

• Custom MDM Hub
• Party Model
• Customer 360
• Consumer Identity
• Snowflake
• dbt
• PostgreSQL
• Advanced SQL
• D&B
• D-U-N-S
• SAP
• MTS
• Sideline
• DAL
• Probabilistic Record Linkage
• Blocking
• Candidate Generation
• Precision/Recall
• Attribute-Level Survivorship
• Reversible Merge/Unmerge
• Crosswalk
• Golden Record
• Hierarchy Edges
• Lineage
• Stewardship
• Reconciliation