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  • Emplois similaires à : Data Engineer (SMTS / LMTS) - MDM
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Data Engineer (SMTS / LMTS) - MDMSalesforce, Inc.. • San Francisco, California, United States
Postuler Maintenant
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Data Engineer (SMTS / LMTS) - MDM

Salesforce, Inc..
  • US
    San Francisco, California, United States
  • US
    San Francisco, California, United States
Postuler Maintenant

À propos

The Experience Salesforce is building the most comprehensive understanding of business relationships across our global customer ecosystem. A critical pillar of this effort is Master Data Management (MDM): the systems that identify, enrich, deduplicate, and govern every business entity and corporate hierarchy we interact with.
We are seeking talented engineers at the SMTS and LMTS levels to join our MDM Engineering organization. In these roles, you will design, develop, and operate critical MDM capabilities — including entity resolution, golden record management, corporate hierarchy automation, and enterprise data integration solutions — while leveraging modern AI technologies to build developer tools, engineering automation, and productivity accelerators across the MDM ecosystem.
At the LMTS level, you will drive technical strategy, architect end-to-end platforms, and provide cross-functional leadership. At the SMTS level, you will serve as a hands‑on technical leader with a bias for action, translating architecture into scalable, production-grade systems. Both roles partner closely with Product Management, Data Governance, Architecture, Systems Integrator partners, and downstream consuming teams.
What You’ll Actually Be Doing
Design, develop, and maintain AI-powered developer tools, engineering automation, and productivity accelerators using modern AI platforms such as Claude, Cursor, Windsurf, GitHub Copilot, and related technologies
Build and maintain end-to-end MDM integration systems, including MuleSoft integrations, Airflow-based workflows, API orchestration layers, event-driven architectures, Change Data Capture (CDC), and batch processing pipelines
Implement entity resolution, golden record lifecycle management, hierarchy processing, data quality validation, and governance capabilities
Build and maintain integrations with third-party data providers such as Dun & Bradstreet, Moody's, and Leadspace to support data enrichment and corporate hierarchy management
Design and optimize data models, database schemas, APIs, and integration patterns supporting MDM business requirements across hierarchical, relational, and party data structures
Build production-grade solutions with strong monitoring, alerting, operational supportability, and security by design
Drive adoption of AI‑assisted software engineering practices to improve developer productivity, testing efficiency, and delivery speed
Participate in design reviews, code reviews, operational readiness reviews, and release activities
Troubleshoot complex production issues, drive root cause analysis, and implement scalable long‑term solutions
Collaborate effectively with globally distributed teams across multiple time zones
LMTS additionally:
Architect and evolve end-to-end MDM integration systems and define technical strategy for entity resolution, golden record lifecycle, hierarchy management, data quality, and governance across multiple business domains
Lead design reviews and establish engineering standards for scalability, observability, resiliency, security, testing, and operational excellence
Partner with PMTS, Product Owners, TPMs, and business stakeholders to define architecture, roadmap priorities, solution designs, and delivery plans aligned with business objectives
Provide technical leadership across internal engineering and systems integrator teams
You're Our Person If... Minimum Qualifications — SMTS (8+ years experience):
8+ years of experience in software engineering, data engineering, enterprise integration, or MDM platforms
Proven experience leveraging modern AI‑assisted development platforms (Claude, Cursor, Windsurf, GitHub Copilot, or similar) to improve engineering productivity
Strong understanding of Generative AI and agentic workflows and their practical application within software engineering organizations
Strong hands‑on experience with Informatica SaaS MDM, particularly with party data models including Account, Contact, Organization, and Supplier
Strong hands‑on development experience with Java, REST APIs, microservices, and enterprise integration patterns
Experience building MuleSoft integrations, API orchestration services, Airflow workflows, ETL/ELT pipelines, and large‑scale data engineering solutions
Experience with Kafka or similar event‑streaming technologies, CDC, and event‑driven architectures
Experience with AWS, GCP, or Azure cloud services and cloud‑native application development
Strong knowledge of SQL, data modeling, database design, and distributed data processing architectures
Excellent communication and collaboration skills
A related technical degree required
Minimum Qualifications — LMTS (10+ years experience):
10+ years of progressive experience in enterprise integration, data engineering, MDM, or large‑scale data platform development
Deep expertise in MDM concepts including entity resolution, golden record lifecycle, match/merge/survivorship, data quality, governance, and hierarchy management
Proven experience designing and building API orchestration layers, MuleSoft integrations, microservices, and event‑driven architectures
Proven experience building developer tools using modern AI tech stack like Claude, Cursor etc. in MDM domain.
Demonstrated technical leadership, cross‑functional collaboration, and ability to drive multiple complex initiatives from design through delivery
Excellent communication skills with the ability to influence stakeholders across all levels of the organization
A related technical degree required
Even Better If...
Experience with Salesforce Data Cloud, CRM platforms, or broader Salesforce ecosystem technologies
Experience working with corporate hierarchy data from Dun & Bradstreet, Moody's, or Leadspace, including family tree traversal, DUNS resolution, monitoring, and registration workflows
Experience building Python‑based data engineering frameworks and automation solutions
Experience with data stewardship, governance processes, and operational data quality tooling
Experience with Retrieval‑Augmented Generation (RAG), vector databases, AI agents, MCP frameworks, or related AI technologies
Experience building internal developer platforms, engineering productivity tools, or agentic solutions supporting enterprise engineering teams
Demonstrated track record of driving measurable engineering productivity improvements through AI‑enabled tooling and automation
Certifications in MDM, data management, cloud platforms, MuleSoft, Informatica, or related technologies
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  • San Francisco, California, United States

Compétences linguistiques

  • English
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