Lead Analytics Engineer - Data Modeling & Quality

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Lead Analytics Engineer - Data Modeling & Quality based in United States.

This is a senior analytics engineering role focused on making healthcare data reliable, scalable, and analytically useful.You will own the SQL and DBT layer that transforms complex clinical and claims data into trusted, production-grade datasets.The role sits at the intersection of advanced data modeling, data quality, healthcare analytics, and client-facing problem solving.You will work closely with Data Engineering, Customer Success, and other technical teams to investigate issues and improve data reliability.Rather than focusing primarily on infrastructure, you will own the logic, structure, validation, and trustworthiness of the data itself.You will also help establish modeling standards, quality frameworks, and operational processes while leveraging AI tools to improve efficiency.This is an opportunity to become a recognized technical leader while directly improving the data foundations that support better healthcare decisions.

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Accountabilities:

• Own data modeling and DBT development: Author, review, refactor, and maintain DBT models across ingestion, bronze, and silver layers using SQL and Spark/Hudi, ensuring datasets are structured, scalable, and production-ready.

• Build data quality into the modeling layer: Develop DBT tests and systematic validation checks to proactively identify issues, while troubleshooting existing problems and strengthening coverage over time.

• Optimize SQL and data models: Investigate slow-running jobs and improve SQL performance, while partnering with Data Engineering on Hudi table design, partitioning strategies, and incremental processing patterns.

• Lead data quality investigations: Triage alerts and tickets across data quality, attribution, hierarchy, and customer-specific issues, distinguishing source-level problems from transformation-layer failures and identifying root causes.

• Design and maintain quality monitoring: Create and maintain volume and data quality monitors covering areas such as null rates, distributions, future dates, entity volumes, field coverage, LOINC coverage, and referential integrity.

• Validate healthcare data: Apply clinical and claims validation rules across silver and gold datasets, assessing data completeness, consistency, coverage, and transformation accuracy.

• Support connector implementation and promotion: Lead data quality reviews during UAT-to-production transitions, evaluating entity coverage, validation results, null patterns, and bronze-to-silver transformation correctness.

• Collaborate cross-functionally: Partner with Data Engineering on root-cause analysis, work with Customer Success on client data concerns, and coordinate with the Measure Implementation Team when quality issues affect healthcare quality measures.

• Communicate findings clearly: Translate technical data-quality investigations into understandable findings and recommendations for clients and non-technical stakeholders.

• Establish modeling standards: Contribute to and enforce consistent data modeling practices across teams, helping raise the quality and maintainability of analytical datasets.

• Drive continuous improvement: Lead technical and process improvement initiatives such as model refactoring, new data quality frameworks, promotion playbooks, and more efficient triage workflows.

• Use AI to improve operations: Incorporate AI tools into day-to-day workflows to investigate data, automate repetitive tasks, improve organization, and increase development and operational efficiency.

• Develop technical leadership: Become a trusted authority on data modeling and quality, independently managing complex work and mentoring or influencing others through strong technical judgment.

Requirements:

• Education: Bachelor's or Master's degree in Computer Science, Statistics, Business, Economics, or a related discipline, or equivalent relevant experience.

• Advanced SQL: Strong command of SQL, including window functions, complex CTEs, aggregation patterns, and performance tuning for columnar databases.

• DBT expertise: Hands-on experience building DBT models, tests, macros, and YAML documentation, with familiarity with incremental modeling strategies.

• Healthcare data knowledge: Working knowledge of healthcare claims data, including professional, institutional, and pharmacy claims, as well as EHR/clinical entities and common healthcare data-quality dimensions.

• Data quality expertise: Ability to systematically investigate data issues, distinguish source problems from transformation failures, design effective validation checks, and determine when distributions or data patterns indicate a problem.

• Technical toolkit: Experience with or willingness to work with technologies such as DBT-Spark, Amazon Redshift, Apache Hudi, AWS Athena, Airflow, Git/GitHub, Grafana, Loki, and Jira.

• Spark and Hudi: Experience with Spark SQL and Hudi table formats is preferred.

• Data quality monitoring: Familiarity with data quality monitoring tools, automated validation frameworks, and observability practices is advantageous.

• Healthcare analytics: Exposure to population health concepts such as HEDIS measures, risk adjustment, and value-based care metrics is a plus.

• Healthcare standards: Familiarity with standards and coding systems such as ICD-10, CPT, NDC, LOINC, and NPI is desirable.

• Python and automation: Python scripting experience for data investigation, automation, or operational workflows is a plus.

• Orchestration: Experience with Argo Workflows, Airflow, or similar orchestration platforms is beneficial.

• AI fluency: Comfortable working in an AI-first environment and motivated to use tools such as Claude to build, verify, and streamline everyday workflows.

• Communication: Strong written and verbal communication skills, with the ability to explain complex technical findings to clients and non-technical stakeholders.

• Analytical judgment: Strong attention to detail and the ability to identify anomalies, assess data patterns, and make sound decisions based on evidence.

• Organization and ownership: Ability to manage multiple projects simultaneously, work independently, prioritize effectively, and take ownership of problems through resolution.

• Leadership mindset: Willingness to become a go-to expert in data modeling and quality while collaborating effectively across technical and business teams.

Benefits:

• Salary: $160,000–$185,000 per year.

• Fully remote: Flexible remote work environment within the United States, supported with the resources needed to perform effectively.

• Meaningful impact: Opportunity to improve the quality, reliability, and trustworthiness of healthcare data used to support patient care and healthcare decision-making.

• Technical growth: Work on complex healthcare data challenges spanning clinical data, claims, analytics, data quality, modeling, and modern data infrastructure.

• AI-first environment: Hands-on exposure to emerging AI tools and the opportunity to help shape how AI is incorporated into technical and operational workflows.

• Leadership exposure: Opportunity to collaborate with and learn from senior leaders while taking ownership of high-impact initiatives.

• Professional development: Build expertise across data modeling, healthcare analytics, data quality, and cross-functional technical leadership.

• Mission-driven culture: Join a diverse, energized, and purpose-driven team working to improve healthcare outcomes through better use of data.

\nHow Jobgether works:

We use an AI-powered matching process to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements. Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company. The final decision and next steps (interviews, assessments) are managed by their internal team.

We appreciate your interest and wish you the best!

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