Data Engineer (Salesforce/SAP)

Role Summary   We are looking for a Data Engineer with a strong focus on data quality to lead continuous improvements to data quality across a platform with a significant Salesforce footprint. This role will be primarily focused on owning the data quality backlog and supporting upcoming data-related priorities as we plan for 2026. You will drive end-to-end resolution of data issues, recommend and help implement data management standards, and partner closely with the squad to shape and execute Salesforce-related data quality user stories—aligned with Salesforce best practices.   Key Responsibilities - Own the Data Quality Backlog 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.   - Drive Resolution of Data Quality Issues Across the Platform 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.   - Salesforce + Data Principles 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).   - Partner Closely with the Squad (Agile Delivery) 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 on Data Strategy and Standards 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.   Required Qualifications - 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.   Preferred Qualifications (Nice-to-have) - 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.   Tools / Tech (Adjustable) - Salesforce (Sales/Service/Health Cloud if applicable), integrations, reporting - SQL and data profiling tools/techniques - Agile tooling: Jira / Azure DevOps - (Optional) Python, dbt, Airflow, DQ/observability tooling