MDM Systems Engineer

Job Title: MDM Systems Engineer

Job Summary:The MDM Systems Engineer is responsible for designing, building, and maintaining the end-to-end integration workflows, automated data pipelines, and system architectures that power our Master Data Management (MDM) ecosystem. Operating at the intersection of data engineering, platform architecture, and business domain requirements, this role ensures seamless, governed data flows ("Data as a Service") between our primary Product Information Management platform (IPIM/Precisely/EnterWorks), Snowflake data warehouse, and downstream consumers (Salesforce, E-Commerce, Tableau). The ideal candidate combines hands-on SQL and data engineering principles with strong systems analysis experience to optimize the master data lifecycle and drive operational efficiency.

Key Responsibilities

1. Integration Engineering & Architecture

·        End-to-End Data Pipelines: Design, implement, and maintain scalable batch and real-time data integration pipelines (APIs, ETL/ELT) connecting source systems (Mainframe, legacy SQL) to MDM/PIM platforms and Snowflake.

·        Source-to-Target System Mapping: Map data lineage, JSON/REST API payloads, and complex business transformation rules across enterprise boundaries to deliver clean, synchronized master data sets.

·        Modernization & Automation: Lead efforts to transition legacy, decentralized transformations (Alteryx, local SQL scripts) into optimized Snowflake cloud tables and automated orchestration workflows.

2. MDM & PIM Platform Management

·        System Integrity & Governance: Supervise and maintain structural data integrity, global taxonomy hierarchies, and attribute schemas within enterprise PIM/MDM platforms (e.g., Enter Works, Precisely or similar), enforcing automated validation rules and business logic.

·        Data Quality Auditing: Write intermediate-to-advanced SQL queries and profiling scripts to perform automated data validation, backend regression testing, and anomaly detection across staging and production environments.

·        Syndication & Outbound Feeds: Configure and manage outbound data syndication feeds to downstream enterprise applications, including Salesforce, e-commerce platforms, and reporting layers.

3. Requirement Engineering & Technical Support

·        Technical Scoping: Deconstruct complex business requirements, stakeholder asks, and domain rules into explicit technical specifications, system flow diagrams, and actionable Jira sprint tasks.

·        Incident Engineering & Root Cause Analysis: Investigate and resolve high-priority data pipeline failures, sync gaps, and integration anomalies submitted via ITSM tools (Ivanti) and Jira, enforcing target SLAs.

·        Standardization & Change Management: Evaluate downstream technical and reporting impacts prior to executing schema updates, taxonomy modifications, or API ingestion changes. Maintain detailed technical documentation, system flows, and data dictionaries in Confluence.

Qualifications & Technical Requirements

·        Education: Bachelor’s degree in Computer Science, Information Systems, Software/Data Engineering, or a related technical field (or equivalent professional experience).

·        Experience: 2–5 years of hands-on experience in data engineering, integration engineering, or systems analysis within an enterprise MDM, PIM, or cloud data warehouse environment.

·        Technical Proficiencies:

o   SQL & Data Querying: Strong proficiency writing complex SQL (joins, aggregations, window functions) for database validation, pipeline profiling, and schema analysis.

o   MDM & Cloud Data Stack: Hands-on experience with enterprise MDM/PIM platforms (EnterWorks, Informatica, or similar) and cloud data platforms (Snowflake).

o   Integration Protocols: Solid understanding of APIs (REST/GraphQL, JSON/XML), ETL/ELT pipeline mechanics, and automated data orchestration.

o   Agile & ITSM Tools: Proficiency with Jira, Confluence, and service desk tools (Ivanti or similar) for managing sprint backlogs and incident tickets.

·        Soft Skills: Strong systems thinking and analytical problem-solving skills, with the ability to bridge communications seamlessly between data engineering teams and non-technical business partners.