Role Description The Senior Data Engineer designs, builds, and maintains scalable data pipelines and architectures to support the Denials AI workflow under the guidance of the Team Lead, Data Management. This role ensures data is reliable, compliant with HIPAA, and optimized. - Collaborate with the Team Lead and cross‑functional teams to gather and refine data requirements for Denials AI solutions - Design, implement, and optimize ETL/ELT pipelines using Python, Dagster, DBT, and AWS data services (Athena, Glue, SQS) - Develop and maintain data models in PostgreSQL; write efficient SQL for querying and performance tuning - Monitor pipeline health and performance; troubleshoot data incidents and implement preventive measures - Enforce data quality and governance standards, including HIPAA compliance for PHI handling - Conduct code reviews, share best practices, and mentor junior data engineers - Automate deployment and monitoring tasks using infrastructure-as-code and AWS CloudWatch metrics and alarms - Document data workflows, schemas, and operational runbooks to support team knowledge transfer - Other duties as assigned - Use, protect and disclose patients’ protected health information (PHI) only in accordance with Health Insurance Portability and Accountability Act (HIPAA) standards - Understand and comply with Information Security and HIPAA policies and procedures at all times - Limit viewing of PHI to the absolute minimum as necessary to perform assigned duties Qualifications - Bachelor’s or master’s degree in Computer Science, Data Engineering, or related field - 5+ years of hands‑on experience building and operating production‑grade data pipelines - Solid experience with workflow orchestration tools (Dagster) and transformation frameworks (DBT) or other similar tools such as Microsoft SSIS, AWS Glue, Air Flow - Strong SQL skills on PostgreSQL for data modeling and query optimization or any other similar technologies (Microsoft SQL Server, Oracle, AWS RDS) - Working knowledge with AWS data services: Athena, Glue, SQS, SNS, IAM, and CloudWatch - Basic proficiency in Python and Python data frameworks (Pandas, PySpark) - Experience with version control (GitHub) and CI/CD for data projects - Familiarity with healthcare data standards and HIPAA compliance - Experience mentoring or leading small technical efforts - Proficiency in Microsoft Office Suite - Strong interpersonal skills, ability to communicate well at all levels of the organization - Strong problem solving and creative skills and the ability to exercise sound judgment and make decisions based on accurate and timely analyses - High level of integrity and dependability with a strong sense of urgency and results oriented - Excellent written and verbal communication skills required Work Conditions - Work Set-Up: Work From Home - Work Schedule: US hours, night shift; must be flexible to accommodate business needs - Physical Demands: - Occasionally required to move around the work area - Sit; perform manual tasks; operate tools and other office equipment such as computer, computer peripherals and telephones - Extend arms; kneel; talk and hear - Mental Demands: - Must be able to follow directions, collaborate with others, and handle stress - Work Environment: The noise level in the work environment is usually minimal Med-Metrix will not discriminate against any employee or applicant for employment because of race, color, religion, sex (including pregnancy, gender identity, and sexual orientation), parental status, national origin, age, disability, genetic information (including family medical history), political affiliation, military service, veteran status, other non-merit based factors, or any other characteristic protected by federal, state or local law.

• Data platform engineering: Design and maintain scalable batch and near-real-time data pipelines across mobile applications, NFC/fuel transactions, station integrations, ERP integrations, payments, support systems, and operational databases. • Data modeling: Create clean, reusable data models for core entities such as customers, vehicles, drivers, stations, transactions, wallets, limits, invoices, products, maintenance services, and geographic coverage. • Reliability and quality: Implement data validation, lineage, observability, alerting, reconciliation, and automated quality checks to ensure business-critical dashboards and reports are accurate and timely. • Analytics enablement: Partner with analytics, product, finance, operations, and customer success teams to deliver self-service datasets, metrics layers, and well-documented data marts. • Performance and cost optimization: Tune queries, storage layouts, orchestration schedules, and cloud resources to improve platform performance and manage infrastructure cost. • Data governance and security: Apply data access controls, PII handling, retention practices, auditability, and compliance-aware engineering patterns across the data lifecycle. • Integration engineering: Build robust ingestion patterns for APIs, webhooks, CDC, files, event streams, third-party integrations, and partner station data feeds. • DevOps for data: Use CI/CD, version control, automated testing, infrastructure-as-code, and deployment standards for data pipelines and transformations. • Incident management: Troubleshoot data incidents, conduct root-cause analysis, reduce recurring failures, and communicate impact clearly to stakeholders. • Technical mentorship: Review designs and code, establish engineering standards, mentor junior team members, and raise the quality bar for data engineering at PetroApp.

• Own Databricks production support for the company's data platform, including monitoring, alerting, and incident response across all production data flows. • Maintain and report on SLA performance metrics for data pipeline delivery, ensuring visibility into platform health and accountability across internal and external stakeholders. • Identify and implement pipeline optimizations that reduce Databricks compute costs, improve throughput, and reduce processing windows while tracking impacts through measurable KPIs. • Migrate legacy ETL/ELT pipelines to Databricks, building automation tooling to reduce manual intervention and ensure uninterrupted data delivery during transitions. • Support new customer onboarding by provisioning, validating, and hardening tenant data pipelines that deliver reliable, isolated data from day one. • Design and build high-performance Databricks pipelines that ingest, transform, and serve ERP and CRM data at scale across both Azure and AWS environments. • Own the Delta Lake architecture, including schema design, partitioning strategies, data quality enforcement, and incremental processing patterns. • Enforce data security best practices across Databricks environments, including role-based access control, secrets management, and compliance requirements for enterprise business data. • Implement data quality monitoring and observability across pipeline health and ML model inputs, ensuring data integrity that directly supports predictive analytics. • Apply and enforce multi-tenant data isolation patterns, ensuring reliable and secure data delivery across enterprise customers. • Partner with the Enterprise Architecture team to ensure data pipelines integrate seamlessly with the broader AI and analytics ecosystem. • Support a globally distributed operation through on-call rotation and after-hours incident response, meeting SLAs across multiple time zones. • Maintain technical documentation, runbooks, and architectural decision records, contributing to team knowledge sharing and operational readiness across on-call and incident response scenarios. • Apply CI/CD best practices to data pipeline development, including version control, automated testing, and deployment tooling to ensure reliable and repeatable pipeline delivery.

Role Description - Entwicklung, Betrieb und kontinuierliche Weiterentwicklung moderner Data-Warehouse-Lösungen - Konzeption, Implementierung und Optimierung von skalierbaren Datenpipelines und zentralen Datenflüssen - Sicherstellung eines stabilen, performanten und qualitativ hochwertigen Datenbetriebs - Analyse komplexer Geschäftsanforderungen und Entwicklung datenbasierter Lösungen für unterschiedliche Fachbereiche - Modellierung und Integration von Daten aus verschiedenen Quellsystemen - Optimierung bestehender ETL-/ELT-Prozesse hinsichtlich Performance, Wartbarkeit und Datenqualität - Mitarbeit bei der Modernisierung von Datenplattformen sowie der Migration bestehender Datenlösungen in Cloud-Umgebungen - Enge Zusammenarbeit mit Fachbereichen, Data Analysts und weiteren Stakeholdern zur Umsetzung neuer Datenanforderungen - Unterstützung bei der kontinuierlichen Weiterentwicklung der Datenarchitektur und der technischen Standards Qualifications - Mehrjährige Erfahrung als Data Engineer mit Fokus auf Data Warehouse - Fundierte Erfahrung im Aufbau, der Weiterentwicklung und dem Betrieb von Data-Warehouse-Lösungen - Sehr gute Kenntnisse in SQL sowie in der Entwicklung und Optimierung von ETL-/ELT-Prozessen - Erfahrung in der Datenmodellierung, beispielsweise nach Data Vault - Erfahrung mit Cloud-basierten Datenplattformen, idealerweise im Microsoft Azure Umfeld - Kenntnisse in Azure Databricks oder vergleichbaren modernen Data-Engineering-Plattformen - Analytische Denkweise sowie die Fähigkeit, komplexe Geschäftsanforderungen in nachhaltige Datenlösungen zu übersetzen - Erfahrung bei der Modernisierung von Datenlandschaften und der Migration von Legacy-Systemen ist ein Plus - Selbstständige, strukturierte und lösungsorientierte Arbeitsweise - Kommunikationsstärke sowie Freude an der Zusammenarbeit mit interdisziplinären Teams - Hohe Eigeninitiative und Verantwortungsbewusstsein - Fliessend in Deutsch und Englisch Company Description Die Coopers Group AG ist eine agile Schweizer Recruiting Agentur, die Spezialisten und Führungskräfte in den Bereichen IT, Life Sciences, Engineering und Finance vermittelt. Mit flexiblen Ansätzen bringen wir Kandidat:innen und Unternehmen zusammen, die nicht nur fachlich, sondern auch menschlich zusammenpassen.