Senior Data Engineer

Empower Pharmacy is a visionary healthcare company dedicated to making quality, affordable medication accessible to millions of patients nationwide. The Senior Data Engineer is responsible for designing, delivering, and continuously improving the enterprise data foundation, enabling faster decisions and scalable growth in a regulated pharmacy environment.

Responsibilities:

• Design and evolve scalable cloud data platforms that support analytics, automation, regulatory reporting, and operational visibility across Empower
• Build resilient serverless patterns, dimensional models, marts, and warehouses that improve data usability, reduce latency, and support AI-enabled consumption
• Own technical decisions from concept through production, balancing speed with security, reliability, cost discipline, long-term maintainability, measurable enterprise adoption, and resilience while strengthening regulated enterprise performance and resilience under sustained hyper-growth pressure daily
• Develop relational and dimensional data models that translate complex business processes into trusted analytical structures for enterprise users
• Apply Kimball, Inmon, Data Vault, and pragmatic modeling approaches where appropriate, using AI-assisted profiling and documentation to accelerate quality reviews
• Ensure models support traceability, performance, governed definitions, and executive decision-making while remaining flexible enough to scale with new products, facilities, compliance requirements, growth, and enterprise reporting expectations across expanding enterprise data domains
• Set high engineering standards for pipeline design, code quality, observability, testing, version control, and deployment discipline
• Use AI tools to accelerate development, identify defects, generate documentation, and improve maintainability without compromising validation or accountability
• Create reusable patterns that help peers deliver faster, safer, and more consistently, while raising the technical maturity of Empower’s data engineering practices across cloud, database, integration, and analytics environments with operational impact and enterprise delivery discipline
• Build, optimize, and maintain ELT and ETL pipelines that ingest structured and semi-structured data from internal systems, external partners, APIs, and operational platforms
• Leverage automation, orchestration, and AI-assisted anomaly detection to improve throughput, quality, and issue resolution
• Own pipeline performance from requirements through monitoring, ensuring data arrives accurately, securely, and on time for analytics, forecasting, compliance, and business-critical decision workflows across high-volume regulated systems
• Design and implement REST API integrations that expand Empower’s data ecosystem while protecting reliability, security, and compliance
• Evaluate source system behavior, authentication patterns, rate limits, error handling, and data contracts, using AI to accelerate mapping, testing, and exception analysis
• Partner with application, infrastructure, and business teams to deliver integrations that support scalable reporting, operational automation, vendor connectivity, faster enterprise insight generation, and business needs securely across digital operations and reporting
• Apply AI and automation to reduce manual data work, accelerate root-cause analysis, improve data quality, and expand self-service capabilities
• Build intelligent checks, metadata-driven workflows, and repeatable validation routines that strengthen confidence in enterprise data products
• Balance innovation with disciplined controls, ensuring AI outputs are reviewed, auditable, and aligned to Empower’s regulated environment, business priorities, and standards for patient, provider, and operational impact as complexity increases across teams, platforms, and workflows
• Embed data quality, lineage, privacy, access control, and documentation into engineering deliverables from the start
• Collaborate with Quality, Compliance, Security, and business stakeholders to ensure data solutions meet expectations for a regulated 503A/503B organization
• Use AI-supported cataloging, profiling, and issue detection to improve transparency while maintaining clear human ownership for definitions, controls, remediation, and decisions that affect regulated operations, executive reporting, and enterprise accountability across Empower’s expanding operating model daily
• Partner with Technology, Operations, Finance, Commercial, Quality, and leadership teams to convert ambiguous needs into prioritized, high-impact data solutions
• Ask sharp questions, challenge weak assumptions, and translate business outcomes into technical architecture, delivery plans, and measurable value
• Use AI to accelerate discovery, scenario analysis, and solution design while maintaining executive-ready communication, stakeholder alignment, and urgency appropriate for Empower’s hyper-growth operating environment and regulated expectations from strategy through durable adoption sustainably
• Mentor engineers, analysts, and cross-functional partners through design reviews, reusable patterns, troubleshooting, and disciplined delivery habits
• Share practical expertise in AWS serverless technologies, databases, data warehousing, visualization enablement, and modern engineering methods

Requirements:

• Minimum 8 years of experience in data engineering, including hands-on ownership of cloud data platforms, data modeling, data warehousing, pipeline development, and enterprise data integration
• Bachelor's degree in Computer Science, Statistics, Informatics, Information Systems, Engineering, or another quantitative discipline; equivalent depth of experience may be considered
• Advanced proficiency in AWS serverless data services, relational and non-relational databases, data warehousing, dimensional modeling, ELT and ETL orchestration, REST APIs, and production-grade engineering practices
• Strong ability to apply AI-assisted development, profiling, testing, documentation, anomaly detection, and workflow automation while maintaining human review, validation discipline, and regulated-environment accountability
• Demonstrated skill translating complex operational, financial, quality, and commercial requirements into scalable data models, pipelines, reporting foundations, and executive-ready technical recommendations
• High learning agility, collaboration, communication, project ownership, and problem-solving capability, with the judgment to balance speed, quality, security, governance, and business impact
• Deep experience with AWS serverless technologies, SQL, Python or comparable programming languages, relational and non-relational databases, ELT and ETL tools, visualization enablement, and production support
• Experience designing governed data solutions in fast-paced, complex, or regulated environments; healthcare, pharmacy, life sciences, or manufacturing exposure is helpful but not required
• Preferred certifications include AWS Data Engineer, AWS Data Analytics, Snowflake, Data Vault 2.0, or demonstrated expertise with Kimball and Inmon modeling methodologies