MDM System Engineer

The MDM Systems Engineer designs, builds, and supports the integration workflows, data pipelines, and system architecture that power our Master Data Management (MDM) ecosystem. This role ensures governed, synchronized product and master data flows between our Product Information Management platform (Precisely EnterWorks), the Snowflake data warehouse, and downstream consumers including Salesforce, e-commerce, and Tableau. The ideal candidate combines hands-on SQL, Python, and data engineering skills with strong systems analysis experience to improve the master data lifecycle across a global, multi-market business., 1. Integration Engineering & Architecture

· End-to-End Data Pipelines: Design, implement, and maintain scalable batch and near-real-time integration pipelines (APIs, ETL/ELT, file-based) connecting source systems (Mainframe, legacy SQL) to Precisely EnterWorks and Snowflake.

· Source-to-Target System Mapping: Produce source-to-target mappings, data lineage, and transformation rules, including JSON/REST API payload mapping across enterprise systems, to deliver clean, synchronized master data sets.

· Modernization & Automation: Lead the migration of legacy, decentralized transformations (Alteryx workflows, local SQL scripts) into Snowflake-native objects and automated, orchestrated pipelines with version control and repeatable deployments.

1. MDM & PIM Platform Management

· System Integrity & Governance: Own structural data integrity, global taxonomy hierarchies, attribute models, and validation rules within Precisely EnterWorks, enforcing automated business logic across markets and languages.

· Data Quality Auditing: Build SQL and Python data-quality checks and profiling scripts for automated validation, backend regression testing, and anomaly detection across staging and production environments.

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

1. Requirement Engineering & Technical Support

· Technical Scoping: Translate business requirements, stakeholder asks, and domain rules into technical specifications, system flow diagrams, and actionable Jira stories.

· Incident Engineering & Root Cause Analysis: Investigate and resolve high-priority pipeline failures, sync gaps, and integration incidents raised through Ivanti and Jira within defined SLAs, and perform root cause analysis to prevent recurrence.

· Standardization & Change Management: Assess downstream technical and reporting impact before schema updates, taxonomy changes, or API ingestion changes. Maintain technical documentation, system flows, and data dictionaries in Confluence.

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

· Experience: 4-8 years of hands-on experience in data engineering, integration engineering, or systems analysis, including at least 2 years on an enterprise MDM or PIM platform.

· Technical Proficiencies:

o SQL & Data Querying: Advanced SQL (complex joins, CTEs, aggregations, window functions) and query performance tuning for validation, pipeline profiling, and schema analysis.

o Snowflake: Hands-on experience with data loading (stages, Snowpipe, COPY/MERGE), incremental processing (Streams/Tasks or equivalent), stored procedures, and role-based access control.

o Python: Python for data processing, API integration, data-quality scripting, and automation.

o MDM/PIM Concepts: Hands-on work with product hierarchies, attribute models, workflows, validation rules, and syndication on an enterprise MDM or PIM platform.

o Integration Protocols: Solid understanding of REST APIs, JSON/XML, ETL/ELT design patterns, and automated data orchestration.

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

· Production Support: Proven incident triage and root cause analysis on production data pipelines.

Preferred Qualifications

· Precisely EnterWorks: Direct experience is a strong plus. Comparable platforms are also considered: Informatica MDM/Product 360, Stibo STEP, Syndigo/Riversand, Reltio, Salsify, or Akeneo.

· Orchestration Tools: Airflow, dbt, Matillion, or AWS services (Lambda, Step Functions, SQS).

· Alteryx: Ability to read, reverse-engineer, and migrate existing Alteryx workflows.

· Mainframe/Legacy Sourcing: Fixed-width flat files, COBOL copybook layouts, or DB2.

· Salesforce Integration: Salesforce APIs (REST, Bulk API) or middleware such as MuleSoft.

· Global Product Data: Multi-language and multi-market product data, including market-specific regulatory or labeling attributes.

· DevOps: Git and CI/CD for data pipeline code and promotion across environments.

· Domain: Retail, CPG, or beauty/consumer products experience.

Soft Skills

· Strong systems thinking and analytical problem-solving skills.

· Communicates clearly with both data engineering teams and non-technical business partners.

· Takes ownership of production issues through to resolution.