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