Data Quality Engineer – Contract

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Wellnecity

WebsiteLinkedInAll Job Openings

11 - 50 employees

Founded 2018

🏥 Healthcare

☁️ SaaS

🤝 B2B

💰 $3.2M Series A - Wellnecity on 2022-01

Healthcare • SaaS • B2B

Wellnecity is the operating system for self-funded health plans that unifies claims, PBM, pharmacy, and point-solution data to provide a single, continuously current source of truth. Its Smart Hub is a real-time intervention and governance engine (not just retrospective reporting) that prioritizes next actions, triggers clinical and operational interventions, and enforces financial controls and documented decision-making to strengthen fiduciary oversight. The platform is used by employers, advisors, and vendors to identify waste, measure vendor performance, and control costs—claiming measurable results such as $1. 4 billion in plan spend managed, $73 million saved last year, and 335,000+ health plan members managed. Wellnecity positions itself as a B2B SaaS solution focused on helping self-funded employers run their health plans like a business with defensible, timely actions that drive ROI.

📋 Description

• Lead data validation and reconciliation efforts for large-scale data migration spanning medical claims, pharmacy claims, and eligibility and enrollment datasets – ensuring accuracy, completeness, and consistency across more than 70 data sources and hundreds of client feeds. • Validate automated field mappings against legacy data warehouse definitions, surfacing schema differences, value-domain mismatches, and transformation gaps, and partnering with Data Engineering to drive resolution. • Execute end-to-end ingestion testing on the new data ingestion platform – running test files, reconciling outputs against legacy baselines and confirming that record counts, field-level distributions, and key business metrics fall within established quality thresholds prior to production cutover. • Design and develop SQL- and Python-based data quality checks, including source-to-target reconciliation queries, distribution comparisons, completeness tests, and parity validation for templating mapping deployments applied across multiple client data feeds. • Identify, document, and triage data discrepancies – including missing records, value mismatches, schema drift, and downstream transformation errors – driving structured resolution across Data Engineering, Product, and platform teams. • Establish and maintain data quality rules and validation thresholds per data source – including field-completeness rates, record-count tolerance, value-distribution expectations, and parity criteria for templated client deployments – to ensure consistent and repeatable validation across the full migration scope. • Document validation logic, sign-off criteria, and known data caveats per source in migration tracking artifacts and run-book documentation, enabling defensible cutover decisions and providing an audit trail for completed migration phases. • Support the transition from migration to steady-state operations by operationalizing data quality checks and monitoring processes, ensuring continuity of validation rigor as each source completes cutover. • Contribute to ongoing data quality management, including identifying patterns, improving validation of workflows, and reducing recurring data issues.

🎯 Requirements

• Bachelor’s degree with 3–5 years of experience in data quality engineering, data operations, or analytics (Master’s may substitute for experience). • Direct experience working with healthcare data (eligibility, enrollment, medical and pharmacy claims). • Advanced SQL proficiency, including writing and optimizing complex queries and experience in design, implementations, and optimization in relational SQL databases. • Strong Python skills with experience building analytical or data validation workflows. • Experience with data cleansing, curation, mining, manipulation, and analysis from disparate systems (SQL, Python preferred). • Demonstrated experience supporting large-scale data migrations, including source-to-target validation and data reconciliation. • Experience validating data pipelines and ETL transformation logic – confirming accuracy of field mappings, derived fields, and aggregated business metrics against expected outputs. • Proven ability to analyze complex, imperfect datasets, identify root causes, and resolve data issues. • Experience owning or contributing to data quality processes, including defining validation logic and maintaining data integrity. • Strong collaboration skills with experience working cross-functionally with data engineering, product, or analytics teams.

🏖️ Benefits

• Health insurance • Flexible work arrangements