Data Engineer Salesforce Data 360

Role overview

This position focuses on building the data foundation that powers Salesforce Data 360 and the Agentforce AI layer. The engineer will ingest and harmonize information from CRM, ERP, warehouse, and event sources, resolve identity across systems, and make the result trustworthy enough to activate against. It is a remote nearshore, contractor role delivered from Latin America, embedded within a client squad and operating as a contributor who challenges assumptions rather than executing tasks.

Responsibilities

- Build and maintain ingestion from CRM, external APIs, object storage, and warehouse platforms, using zero-copy patterns where available. - Map source data into the platform's canonical model and maintain the underlying data streams. - Design and tune identity resolution rulesets, measuring actual match rates rather than assuming them. - Build calculated insights, segments, and activation targets that marketing and service teams consume. - Model data so AI agents can be grounded on it, including retrieval-ready unstructured content. - Implement consent, retention, and residency handling as part of the data model, not as an afterthought. - Build and orchestrate transformation pipelines in SQL and Python, with tests and lineage. - Monitor freshness, volume, schema drift, and cost, and alert on anomalies. - Tune for consumption-based platform cost and report what each pipeline actually spends.

Requirements

- Bachelor's degree in Computer Science, Data Engineering, Information Systems, or a related field, or equivalent practical experience. - At least 5 years of data engineering experience with production pipeline ownership in an Agile environment. - Working understanding of the Salesforce CRM data model and how it differs from a warehouse model. - Hands-on experience with Salesforce Data 360, including data streams, data model objects, identity resolution, calculated insights, segmentation, and activation. - Strong SQL proficiency, including window functions and query tuning. - Python for transformation, orchestration, and API integration. - Experience with at least one cloud data warehouse such as Snowflake, BigQuery, Redshift, or Databricks. - Knowledge of dimensional and canonical modeling: star schemas, slowly changing dimensions, and surrogate keys. - Experience with data quality, lineage, and observability tooling. - Advanced or fluent English (C1/C2) for daily technical collaboration.

Nice to have

- Background grounding AI agents with structured and unstructured data. - Familiarity with privacy-by-design patterns on modern data platforms.

Benefits and work setup

- Remote nearshore role based in Latin America, working during the client's core business hours with substantial time-zone overlap. - Full-time contractor agreement. - Applicants must be permanently authorized to work in their country of residence; visa sponsorship is not offered.