Data Operations, Sr. Manager

Reports to:  Director of Data 

JOB OVERVIEW  

We are seeking a hands-on, customer‑facing Data Operations Leader to oversee and mature our data operations function. This role combines technical depth with operational leadership—monitoring daily data workflows, working directly with healthcare clients, and partnering across Product, Analytics, Governance, and Engineering to deliver scalable, high‑quality data solutions. 

The ideal candidate is comfortable using SQL, python, spark, and orchestration tools to triage operational issues, dig into file-level details, and improve reliability through automation. This candidate has experience creating tools to improve data observability, and documentation, as well as demonstrated experience utilizing AI to improve operational efficiency. This person acts as a “player‑coach” and will need to balance hands-on technical work, coaching the Data Operations team, and working directly with internal stakeholders. 

Location: Dallas, TX - Hybrid schedule (3x in office per week)

Responsibilities:  

As the leader of the Data Operations Team: 

• Communication & Engagement 
  • Partner with internal teams and external clients to align on data formats, specifications, and integration requirements. Provide clear explanations of healthcare file logic and proactively communicate issues, timelines, and resolutions to reduce escalations. 
• Operational Ownership & Responsiveness 
  • Monitor daily data pipelines, alerts, and file transfers; triage and resolve issues quickly to maintain reliable operations. Take ownership of cross-functional issues, even when the root cause spans multiple teams, and ensure stakeholders are informed throughout. 
• Process Improvement & Automation 
  • Identify manual steps in file processing, validation, and monitoring and replace them with automated, scalable solutions using Databricks, Python, and Azure tooling. Drive initiatives that reduce operational overhead and improve self-service, including automated QA, schema validation, and observability enhancements. 
• Monitoring, Data Quality & Observability 
  • Implement and maintain robust monitoring to detect anomalies in inbound and outbound healthcare files, including 834/837 and flat file formats. Expand logging, alerting, and data quality frameworks to catch issues early and minimize client impact. 
• Technical Leadership 
  • Provide hands-on leadership and coaching with SQL, data pipelines, and healthcare data structures—including reviewing notebooks, debugging SQL, validating transformations, and guiding root-cause investigations. Collaborate with Engineering, Cloud Infrastructure, and Governance on scalable architecture and DevOps/Data DevOps best practices. 
• Team Leadership & Coordination 
  • Lead the Data Operations team through daily standups, clear prioritization, and coaching. Ensure runbooks, documentation, and onboarding materials are current, and support the team in resolving incidents and onboarding new data assets. 

Requirements: 

Education & Experience 

• Bachelor’s degree in Computer Science, Engineering, or a related field. 

• 5+ years of experience in SQL with the ability to write complex queries. 

• 3+ years of experience performing ETL and data engineering tasks. 

• 3+ years of experience with Big Data technologies such as Databricks, Python, and Data Lakes. 

Technical Skills 

• Proficiency in: 

• SQL 

• Azure Data Services (including Azure Data Factory) 

• Databricks 

• Python 

• Strong understanding of DevOps/Data DevOps concepts. 

• Experience with data observability tools and practices. 

• Familiarity with data quality frameworks and automation tools. 

Healthcare Domain Experience 

• Experience with healthcare EDI formats (834, 837, etc.) 

• Understanding of eligibility, claims, accumulators, and healthcare reporting concepts 

• Familiarity with PHI handling, HIPAA considerations, and governance practices 

Core Competencies 

• Ability to monitor, diagnose, and improve data observability and integrity  

• Capable of handling ad hoc data investigations and resolving complex issues  

• Strong documentation habits, including runbooks, specs, and process guides  

• Skilled at onboarding and validating new data feeds and assets  

• Strong analytical and problem-solving skills 

Soft Skills & Intrinsics 

• Excellent communicator—able to translate technical concepts for clients and internal partners  

• Strong cross-functional collaborator with Product, Analytics, Governance, Engineering, and Infrastructure  

• Adaptability to evolving business models and healthcare needs  

• Customer-first mindset with the ability to engage confidently on client calls 

Strong Candidates Will:  

• Have SQL/Python experience specific to healthcare data  

• Be comfortable acting as both an individual contributor and team leader (“player coach”)  

• Demonstrate experience improving operational reliability, automation, or observability  

• Value diversity, inclusion, and collaborative teamwork 

Benefits

Analytics Engineer

Reports to:  Manager, Analytics Engineering 

JOB OVERVIEW  

The Analytics Engineer is a hands-on individual contributor within Lantern’s centralized Analytics Center of Excellence (CoE), responsible for building, testing, releasing, and maintaining shared analytical data models that enable a distributed analytics community to confidently self-serve insights. The role partners with analysts and analytics leaders across the business to identify common needs, close data gaps, and improve the reliability and consistency of analytics. This is a builder role focused on durable, reusable data products, not one-off analysis. 

Success is measured by data quality, adoption of certified models, smooth analytics releases, and reduced ambiguity in business reporting.

Location: Dallas, TX - Hybrid schedule (3x in office per week)

Responsibilities:  

Data Modeling & Analytics Engineering 

• Design and maintain fact and dimension models optimized for dashboards, reporting, and self-service analytics in Databricks using dbt. 

• Explore and onboard new or external data sources, developing Bronze and Silver layer models that make data usable by analysts. 

• Implement approved "single source of truth" KPI logic in Silver and Gold models, ensuring accuracy, consistency, and maintainability. 

• Maintain clear documentation to support adoption and correct usage by the analytics community. 

• Apply established SDLC practices, including Gitbased version control, code reviews, and participation in QA/UAT. 

Collaboration & Enablement 

• Partner with Business Analysts, Finance, Operations, and Strategy teams to translate recurring analytics needs into scalable, shared models. 

• Act as an enablement resource for a distributed analytics community by answering questions, providing guidance, and coaching on data usage and SQL best practices. 

• Improve shared data assets based on analyst feedback and usage patterns rather than building one-off solutions. 

Production Support & Deployment 

• Implement automated data tests (schema, freshness, and business logic) as part of regular development work. 

• Support and lead analytics releases, including regression testing, release documentation, release notifications, and post release validation. 

• Perform level1 triage of data quality and performance issues, resolving issues where possible and escalating to Data Engineering when required. 

• Collaborate with Data Engineering to address source data issues and identify performance optimizations. 

Requirements 

Required 

• 3–6 years of experience in analytics engineering or closely related data roles. 

• Strong SQL skills with experience delivering production analytical data models. 

• Handson experience with dbt (Core or Cloud). 

• Experience with cloud data platforms such as Databricks or Snowflake. 

• Experience working with regulated data (PII/PHI). 

• Ability to communicate data changes and assumptions clearly to nontechnical partners. 

• Experience working with large datasets, performing data validation, comparison, and reconciliation tasks. 

• Strong proficiency in Microsoft Excel for data analysis, reconciliation, and reporting. 

Preferred 

• Experience with medallion or layered data architectures. 

• Exposure to automated data quality tooling (e.g., dbt tests, Soda). 

• Familiarity with orchestration and deployment workflows (Airflow, Azure Data Factory). 

• Experience supporting Finance, Operations, or Commercial analytics use cases. 

Benefits

Data Engineer - Governance and QA

The Healthcare Data Reporting team delivers high‑quality outbound files and data feeds to clients and exchange partners across claims, utilization, accumulator, and other enterprise health data domains. We are seeking a Data Engineer with deep experience in data governance and QA automation to take ownership of data quality across our reporting pipelines and configuration-based reporting suite.

This role ensures the accuracy, stability, and trustworthiness of our outbound data products by establishing automated data quality frameworks, improving reporting pipelines, and governing data contracts. While a small part of the role involves hands-on manual QA, the majority focuses on building scalable systems, frameworks, and automated testing strategies that ensure our data is trusted, secure, and production-ready.

This is a highly technical role ideal for someone who thrives at the intersection of data engineering, software QA, automation, and quality governance.

Location: Hybrid - Dallas, Texas highly preferred. Hybrid schedule is expected to be in office 3x a week at minimum. 

Responsibilities:

Data Quality & Validation

• Own end‑to‑end data quality, integrity, and reliability across staging, transformation, and outbound reporting layers.
• Ensure deterministic logic, repeatability, and consistent outcomes across reporting pipelines and configuration‑driven reporting assets.
• Implement automated data quality checks using Python‑based frameworks (dbt tests, Pytest, Soda, Great Expectations, or similar).
• Enforce data contracts and validation rules for all outbound files and client deliverables.

Test Strategy & Automation

• Define and execute the overall test strategy for outbound reporting, including unit, integration, regression, and end‑to‑end testing.
• Build and maintain automated test suites to validate field mappings, transformation logic, and reporting configurations.
• Integrate automated QA processes into CI/CD pipelines in partnership with Platform Engineering.
• Ensure all pipelines and data products are testable, observable, and instrumented for automated quality checks.

Cross-functional Collaboration

• Partner with Data Engineering, Platform, and centralized QA teams to align on testing standards, frameworks, and best practices.
• Provide subject matter expertise on data quality, pipeline testing, and reporting logic across the enterprise.
• Influence architectural decisions related to data models, reporting pipelines, and configuration‑driven report generation.

Quality Governance & Standards

• Establish and maintain clear QA documentation, including test plans, cases, validation rules, and data quality SLAs.
• Implement version control, automated validation scripts, and monitoring dashboards to support scalable quality governance.
• Contribute to continuous improvement of data governance, quality controls, and reliability engineering practices.

Targeted Manual Validation (As Needed)

• Perform manual QA for new report configurations, schema changes, mapping logic, and first‑time outbound file launches where automation is insufficient.
• Validate SQL transformations, metadata, and schema consistency across reporting assets.
• Document defects, track resolution, and lead root‑cause analysis for data quality issues.

Required Qualifications:

• Bachelor’s degree in Computer Science, Information Systems, Data Engineering, or related field — or equivalent experience.
• 5+ years of experience in QA Engineering, Data Engineering, or Data Quality within data‑intensive or regulated environments (healthcare preferred).
• Python experience for automated testing, data validation, and quality frameworks.
• Hands‑on experience with automated data quality/testing tools (dbt tests, Pytest, Soda, Great Expectations, or similar).
• Experience working within CI/CD environments (GitHub Actions, GitLab, Jenkins, etc.).
• Strong understanding of data modeling and data architecture concepts (dimensional, normalized, and reporting models).
• Excellent analytical, troubleshooting, and root‑cause analysis skills.
• Clear communication skills with the ability to translate technical findings into business context.
• High attention to detail with a strong sense of ownership for data accuracy and reliability.

Preferred Qualifications:

• Experience with healthcare datasets (claims, eligibility, utilization, accumulators).
• Familiarity with cloud platforms (AWS, Azure, or GCP) and modern data platform components.
• Experience with Databricks for data processing, testing, and pipeline automation.
• Experience with data quality SLAs, observability tooling, or data reliability engineering.
• Background in configuration‑driven reporting or client‑specific outbound file generation.

Benefits:

Technical Business Analyst – Healthcare Data Reporting

The Technical Business Analyst – Healthcare Data & Reporting will serve as a critical liaison between our technical/reporting teams several client-facing departments, including Client Success, Implementation, Consultant Relations, and Partnerships. This role ensures that complex healthcare data is accurately translated into actionable insights and high quality deliverables for our clients and other external partners. The ideal candidate excels at bridging business needs with technical capabilities, driving clarity, efficiency, and continuous improvement across the reporting lifecycle.

Location: Hybrid - Dallas, Texas highly preferred. Hybrid schedule is expected to be in office 3x a week at minimum. 

Responsibilities:

 Business & Stakeholder Engagement

• Act as the primary conduit between outbound reporting team and client-facing groups.
• Facilitate requirement gathering conversations, translate business needs into clear technical specifications, and ensure mutual understanding across teams.
• Work with stakeholders to refine requirements for intake requests into the outbound client reporting team.
• Communicate complex healthcare data concepts in a clear, accessible manner for non technical audiences.

Reporting Workflow & Operational Management

• Manage intake workflows for the client reporting team, ensuring prioritization, clarity, and timely delivery of requests.
• Partner closely with the Healthcare Data Reporting Senior Manager to design, implement, and continuously improve operational efficiency initiatives across the team.
• Define, implement, and monitor KPIs and success metrics for the development and reporting teams.

Healthcare Data Analysis

• Analyze and interpret healthcare data related to accumulators, claims, eligibility, and benefit structures.
• Understand end to end data flows—from internal systems and ETL processes through to final external client reports.
• Identify data quality issues, validate report outputs, and support troubleshooting efforts.

Technical & Analytical Deliverables

• Write SQL queries to validate data, troubleshoot, or perform exploratory analysis.
• Collaborate with developers, data engineers, and BI analysts to support the creation of dashboards, visualizations, and client ready reports.
• Assist with development, testing, training, and documentation of Power BI dashboards and Databricks data processes.

Requirements:

• Strong understanding of healthcare data, including claim files, accumulators, healthcare plan structures, and standard industry datasets.
• Demonstrated ability to translate business requirements into technical specifications.
• Excellent written and verbal communication skills, with the ability to engage both technical and non technical stakeholders.
• Proven analytical and problem solving abilities with strong attention to detail.
• Ability to manage competing priorities, coordinate workflows, and drive timely outcomes.

Strong Candidates Will Have:

• Experience in a healthcare payer, TPA, pharmacy benefits, or healthcare analytics environment.
• Prior involvement in operational process improvement or KPI development.
• Familiarity with ETL concepts, data pipelines, or similar data engineering processes.
• Background supporting custom reporting teams or client-facing analytics programs.

Benefits: