Identity / MDM Specialist

Blend360•Columbia, MD

7d•Hybrid

About The Position

Blend is looking for an Identity / MDM Specialist to architect and implement the foundational layer that makes intelligent, data-driven decisions possible. This is a high-impact engineering role at the heart of our data platform you will design how customer identities are resolved, how households are linked, and how golden records are maintained across a complex multi-source environment. If you have deep experience untangling messy, real-world identity data and a track record of building matching systems that actually work at scale, this role was designed for you.

Requirements

8+ years in data engineering with at least 4 years focused on identity resolution or MDM
Hands-on experience with MDM platforms and graph data models for entity resolution
Proven track record building probabilistic matching algorithms in production environments
Deep expertise in PII handling, data privacy compliance, and secure data practices
Experience integrating third-party identity enrichment data (e.g. Acxiom, LiveRamp, or similar)
Identity resolution frameworks and MDM platforms (e.g. Informatica, Reltio, D&B MDM, or custom-built)
Probabilistic matching: blocking strategies, similarity scoring, ML-assisted entity resolution
Graph data models for household and relationship mapping
Data quality tooling and profiling at scale
SQL, Python, and distributed data processing (Spark, Databricks, or equivalent)
Ability to operate independently and make architectural decisions with limited oversight
Strong written and verbal communication — you can explain a confidence score to a product manager
Collaborative approach to cross-functional work with engineering, analytics, and vendors

Nice To Haves

Experience in media, telecommunications, or subscription-based product environments
Familiarity with graph databases (Neo4j, Amazon Neptune) for relationship modeling
Exposure to real-time identity resolution pipelines
Experience building identity resolution on cloud-native stacks (AWS, GCP, Azure)

Responsibilities

Design and implement customer matching logic using both deterministic and probabilistic approaches
Build a household linkage model that connects individual subscribers to household units
Implement deduplication logic across multiple heterogeneous data sources
Define golden record structure, confidence scoring, and merge/split rules
Establish data quality frameworks and monitoring to ensure identity integrity over time
Define PII handling standards, lineage tracking, and audit trails for all identity data
Develop confidence thresholds and escalation paths for low-confidence matches
Coordinate with third-party identity enrichment vendors on data formats and matching keys
Partner with product, engineering, and analytics teams to align on unified customer definitions
Document architecture decisions, match rules, and operational runbooks