Data Engineer Salesforce Data 360

aspenviewtech • Buenos Aires, Autonomous City of Buenos Aires, Argentina, Bogotá, Bogota D.C., Colombia

No Relocation

Posted: September 24, 2026

Job Description

Why Join AspenView?

At AspenView, we’re more than a nearshore IT partner, we’re a people-first, purpose-driven company that believes great culture drives great outcomes. We’re passionate about connecting talent and technology to deliver measurable value for clients, and meaningful career paths for our people.

What You Will Do

About the Role
We are seeking a Data Engineer to build the data foundation that Salesforce Data 360 (formerly Data Cloud) and Agentforce depend on. You will ingest and harmonize data from CRM, ERP, warehouse, and event sources into Data 360, resolve identity across them, and make the result trustworthy enough to activate against. Most AI work on Salesforce fails on data, not on models, which makes this role a critical enabler of the program rather than a support function.
This is a full-time, contractor agreement, remote nearshore position delivered from AspenView’s Latin American delivery centers, working the client’s core business hours with substantial time-zone overlap. You will be part of the client’s squad under AspenView Delivery Leadership, which holds the employment relationship and delivery accountability. This is a contributor role, not a task-execution role, the team needs someone who questions whether the right thing is being built, and says so.

What You Bring

Ingestion & Harmonization

Source Ingestion: Build ingestion from Salesforce CRM, external APIs, object storage, and warehouse sources—including Snowflake, BigQuery, or Databricks via zero-copy where available.
Canonical Mapping: Map source data to the Data 360 canonical model and maintain the data streams behind it.
Identity Resolution: Design and tune identity resolution rulesets, measuring the match rate rather than assuming it.

Modeling & Activatio

Insights & Segments: Build calculated insights, segments, and activation targets that marketing and service teams can use.
Agentforce Grounding: Model data for grounding Agentforce agents, including retrieval-ready unstructured content.
Privacy by Design: Implement consent, data-retention, and residency handling as part of the model, not after it.

Pipeline Engineering

Transformation Pipelines: Build and orchestrate transformation pipelines in SQL and Python, with tests and lineage.
Observability: Monitor freshness, volume, schema drift, and cost, and alert on them.
Cost Optimization: Tune for Data 360 consumption-based cost and report what each pipeline actually costs.

Education

Bachelor’s degree in Computer Science, Data Engineering, Information Systems, or a related field—or equivalent experience.

Experience

5+ years in data engineering with production pipeline ownership in an Agile environment.
Understanding of the Salesforce CRM data model and how it differs from a warehouse model.
Experience with data quality, lineage, and observability tooling

Technical Expertise

Salesforce Data 360: Data streams, data model objects, identity resolution, calculated insights, segmentation, and activation.
SQL: Strong proficiency, including window functions and query tuning.
Python: Transformation, orchestration, and API integration.
Cloud Data Warehouses: Snowflake, BigQuery, Redshift, or Databricks.
Data Modeling: Dimensional and canonical modeling: star schemas, slowly changing dimensions, and surrogate keys.

Language Proficiency

Advanced/Fluent English (C1/C2) for daily technical collaboration with Boston-based technology and research leadership.

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