Data Engineer, MDM

Salesforce

Confirmed live today High trust

Hybrid

Quick summary

Work type: Hybrid
Location: San Francisco, CANew York, NYSeattle, WAIndianapolis, IN
Employment: Full-time
Posted: 22 days ago
Freshness: Confirmed live today
Nearby: 99+ roles within 25 mi

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Salary context

How this pay compares to similar roles

Similar $170k

$116k $223k

below market most similar roles pay here above market

This listing doesn't post a salary. Most similar roles pay $126,800–$213,000.

Based on 240 similar postings.

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About Salesforce

Salesforce is the world''s leading customer relationship management (CRM) platform, offering cloud-based software for sales, service, marketing, analytics, and application development. Industry: Enterprise Software & Cloud Computing

Salesforce currently has 221 open roles on FindRole.

Listed pay typically runs $148,500–$260,100 across 125 roles with salary data.

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At a glance

TL;DR · Data Engineer, MDM

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The Data Engineer (SMTS / LMTS) - MDM joins the MDM Engineering organization to build systems that identify, enrich, deduplicate, and govern business entities and corporate hierarchies. In these roles, you will design, develop, and operate critical capabilities including entity resolution, golden record management, and corporate hierarchy automation. You will build AI-powered developer tools and engineering automation using platforms like Claude, Cursor, Windsurf, and GitHub Copilot. Key responsibilities include maintaining MuleSoft integrations, Airflow workflows, API orchestration layers, and event-driven architectures using Kafka and CDC. You will also manage integrations with third-party providers like Dun & Bradstreet and Moody's. Required skills include Java, REST APIs, microservices, SQL, and experience with Informatica SaaS MDM, AWS, GCP, or Azure cloud services.

What does a Data Engineer earn in California?

Median $187700 from 41 postings across 21 companies.

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Skills

Master Data Management (MDM) Informatica SaaS MDM Java Python MuleSoft Airflow Kafka SQL REST APIs Microservices AWS GCP Azure CDC ETL/ELT Event-driven Architecture Generative AI RAG Vector Databases GitHub Copilot Claude Cursor Windsurf Data Modeling API Orchestration

What you'll do

Design, develop, and maintain AI-powered developer tools and engineering automation using platforms like Claude and GitHub Copilot.
Build and maintain end-to-end MDM integration systems including MuleSoft integrations, Airflow workflows, and event-driven architectures.
Implement entity resolution, golden record lifecycle management, hierarchy processing, and data quality validation capabilities.
Integrate third-party data providers like Dun & Bradstreet and Moody's to support data enrichment and corporate hierarchy management.
Design and optimize data models, database schemas, and API patterns for hierarchical and relational data structures.
Build production-grade solutions with integrated monitoring, alerting, operational supportability, and security by design.
Troubleshoot complex production issues, perform root cause analysis, and implement scalable long-term technical solutions.
Lead technical strategy, architect end-to-end platforms, and establish engineering standards for scalability and observability.

What we're looking for

A related technical degree is required.
SMTS level requires 8+ years of experience in software engineering, data engineering, enterprise integration, or MDM platforms.
LMTS level requires 10+ years of progressive experience in enterprise integration, data engineering, MDM, or large-scale data platform development.
Proven experience leveraging modern AI-assisted development platforms like Claude, Cursor, Windsurf, or GitHub Copilot is required.
Strong hands-on experience with Informatica SaaS MDM, Java, REST APIs, microservices, and enterprise integration patterns is required.
Experience building MuleSoft integrations, Airflow workflows, ETL/ELT pipelines, and Kafka or similar event-streaming technologies is required.
Experience with AWS, GCP, or Azure cloud services and strong knowledge of SQL and data modeling is required.
Experience with Salesforce Data Cloud, Python-based data engineering, or RAG and vector databases is preferred (preferred).