Technology

EDT Solution Architect

Saicon Consultants Inc.Pleasanton, CA🇺🇸United StatesPosted Oct 2, 2026

Why This Role Stands Out

This hybrid role offers a fantastic opportunity to architect and build the foundational data and AI solutions that drive impactful workforce insights and responsible AI development. You'll thrive here if you're a skilled architect eager to collaborate across teams and contribute to a reputable company's innovative data strategy. Apply now to shape the future of people analytics!

Quick Overview

Seniority

Mid Senior

Work mode

Hybrid

Location

Pleasanton, CA, United States

Posted

21 hours ago

SQLETLDatabricksPython

Job Description

ROLE PURPOSE:
Build the trusted data and AI foundation that powers people analytics, workforce insights, and responsible AI-enabled solutions.

Your role at Clorox:
The People Data & AI Engineer builds and operates the trusted data and AI foundation that powers people analytics, workforce insights, and emerging AI-enabled solutions. This role designs scalable people-data architecture, integrates data across HR and enterprise platforms, establishes proactive quality and lineage controls, and ensures the reliability and governance of the People Data platform.
Working within the People Analytics team and in close partnership with the Enterprise Data Team, HR Technology, Security, and People functional teams, this role translates prioritized business and product requirements into secure, governed, and production-ready data solutions.
In this role, you will lead:
People Data Architecture and Engineering

Design, build, and maintain scalable people-data models within the enterprise data warehouse.
Develop and support data pipelines, APIs, interfaces, and integrations connecting Workday and other People systems with the enterprise data platform.
Translate approved business definitions and product requirements into technical data specifications, structures, and reusable data assets.
Establish architecture patterns that enable consistent use of people data across dashboards, analytics products, scorecards, and approved AI use cases.
Partner with the Enterprise Data Team on source-data onboarding, engineering dependencies, release planning, and production implementation.
Maintain technical documentation for data models, integrations, transformation logic, dependencies, and platform components.

Data Quality, Lineage, and Reliability

Establish automated data-quality monitoring, validation rules, reconciliation controls, and exception alerts for critical people-data elements.
Implement and maintain end-to-end data lineage, including source, transformation, calculation, and downstream consumption.
Define production support, incident management, and escalation practices for people-data products.
Monitor platform health, pipeline performance, refresh reliability, and recurring failure patterns.
Reduce reliance on manual, person-dependent data checks through repeatable and observable controls.

Data Governance, Privacy, and Access

Implement technical controls that support approved access, privacy, confidentiality, retention, and sensitive-data handling standards.
Partner with People Analytics leadership, Privacy, Legal, Security, HR Technology, and the Enterprise Data Team to operationalize people-data governance requirements.
Contribute technical definitions, source mappings, transformation logic, and lineage information to the people-data dictionary.
Ensure changes to sensitive people-data structures are appropriately reviewed, tested, documented, and released.

5+ years of progressive experience in data engineering, analytics engineering, data architecture, or a related field, including experience building production-grade data pipelines, models, integrations, and quality controls.
Experience with HR data, Workday, enterprise data warehouses, sensitive-data governance, or AI/ML data enablement is strongly preferred.
Technical capabilities: SQL, Python, data modeling, ETL/ELT, APIs, cloud data platforms, version control, automated testing.
Platform preferences: Experience with Workday data, Databricks or a comparable cloud data ecosystem, and BI semantic models.

Education/equivalent experience: