Data Engineer – Salesforce/SAP

Resilient Co.

Data Engineer focused on data quality to improve Salesforce-related data integrity and standards. Collaborating closely with Agile teams and driving continuous improvements.

Posted 4/20/2026contractRemote • 🇦🇷 ArgentinaMid-LevelSeniorWebsite

Tech Stack

Tools & technologies

AirflowETLPythonSQL

About the role

Key responsibilities & impact

Own the end-to-end data quality backlog: intake, analysis, prioritization, definition, tracking, and closure.
Define severity/impact criteria (patient, compliance, operational, reporting) and SLAs with key stakeholders.
Perform root cause analysis for issues such as duplicates, missing/invalid values, inconsistent definitions, referential integrity problems, mapping errors, and out-of-standard data.
Implement remediation actions: validation rules, deduplication, normalization, reconciliation, and preventive monitoring.
Design and maintain data quality controls (tests, rules, scorecards) and alerting to prevent recurrence.
Work confidently with standard/custom objects, relationships, security/access model considerations, integrations, and processes impacting data quality.
Ensure solutions follow Salesforce data management standards and best practices (governance, naming conventions, ownership, stewardship, lineage/traceability where applicable).
Collaborate with PO/BA/Engineers/QA to turn data issues into actionable Salesforce-related data quality user stories.
Lead/participate in refinement: scope definition, acceptance criteria, test approach, release plan, and validation.
Support execution and verify outcomes in lower environments and production.
Provide guidance and recommendations on data strategy, data management standards, and sustainable quality practices.
Propose a 2026 roadmap of quick wins and structural improvements to improve reliability and reduce recurring issues.

Requirements

What you’ll need

Proven experience as a Data Engineer working on data quality (identification, prioritization, resolution, prevention).
Strong understanding of Salesforce and its data model (objects, relationships, integrations, reporting) and industry best practices for Salesforce data management.
Strong SQL skills for investigation and validation (profiling, complex joins, reconciliation).
Experience working in Agile teams and managing a backlog (user stories, acceptance criteria, Definition of Done).
Strong communication skills to explain findings, risks, and impact to technical and non-technical stakeholders.
Experience with data quality/observability practices: automated tests, monitoring, DQ dashboards/scorecards, alerting.
Experience with data integration and pipelines (ETL/ELT) and analytics ecosystems (e.g., Python, dbt, Airflow) depending on the stack.
Familiarity with data governance practices (data catalog, lineage, definitions, stewardship).
Exposure to healthcare privacy/security or regulatory considerations (e.g., HIPAA), depending on region/client.

Benefits

Comp & perks

Health insurance
Professional development opportunities

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills & Tools

data qualitySQLdata integrationETLELTdata governancedata managementdata modelingautomated testsdata pipelines

Soft Skills

communicationcollaborationproblem-solvingleadershipanalytical thinkingprioritizationstakeholder managementguidancerecommendationadaptability