Data Engineer

Job Description

Job Description

Salary:

Who We Are

AtVPLwe modernize clinical supply chains to support healthier patients. Our technology-driven solutions and consultative customer experience empower health systems and outpatient pharmacies to build smarter, more resilient supply chains. With over 700 hospitals and a 97 percent customer retention rate,weretrusted to deliver transparency, cost savings, and peace of mind.

WhatWereLooking For

VPL is looking for a hands-on Data Engineer who can combine strong engineering fundamentals with analytical problem-solving. You will help keep our currentMicrosoftSQL Server and SSIS data environment reliable while developing the skills and platform knowledge needed to support VPLs transition to PostgreSQL and Snowflake.

This role supports both our enterprise data warehouse and production-critical ETL processes. The right candidate can troubleshoot a failed pipeline,optimizeSQL, reconcile operational or financial data, and work with business partners to turn complex questions into trusted data solutions. Extensive Snowflake experience is notrequired; we value strong SQL, data engineering, and analytical capabilities with the ability and desire to grow into Snowflake and modern ELT practices.

WhatYoullDo

• Operate, monitor, troubleshoot, and improve SSIS packages and scheduled ETL jobs that support VPLs enterprise data warehouse and production systems.
  
• Diagnose pipeline failures, performance bottlenecks, data discrepancies, dependency issues, and incomplete or delayed loads; restore processing safely and document recurring solutions.
  
• Develop and optimize complex T-SQL queries, stored procedures, views, functions, database jobs, and data transformations in Microsoft SQL Server.
  
• Design, build, test, and
  maintaindata pipelines that integrate operational,logistics, customer, carrier, supplier, billing, and financial data.
• Develop and
  maintaindimensional models, relational models, reporting datasets, and other analytical data structures that areaccurate, understandable, and performant.
• Work with analysts, product managers, engineers, finance, operations, and other business partners to define metrics, business rules, source-to-target mappings, and data-quality expectations.
  
• Reconcile data across source systems, production applications, financial processes, and analytical platforms; investigate anomalies and explain findings clearly.
  
• Implement controls and alerts that
  identifymissing, duplicate, late, incomplete, or inconsistent data before it affects customers, reporting, or billing.
• Improve pipeline reliability through
  appropriate logging, monitoring,restartability, error handling, testing, source control, documentation, and deployment practices.
• Help evaluate which existing SSIS workloads should be migrated, redesigned, replaced, or retired as VPL moves toward PostgreSQL, Snowflake, and modern ELT patterns.
  
• Contribute to data migration, validation, parallel testing, and cutover activities while protecting the accuracy and continuity of critical data processes.
  
• Use SQL, Python, statistics, visualization, or other analytical techniques to profile data, test assumptions,
  identifypatterns, and support business decision-making.
• Maintain data lineage, technical documentation, runbooks, recovery procedures, and definitions for critical datasets, transformations, and business rules.
  
• Follow VPL security, privacy, governance, and compliance requirements and
  assistwith audit or customer-assurance evidence related to data processing.
• Participate in after-hours
  support for production-critical data processes after completing onboarding anddemonstratingreadiness.

WhatYoullBring

• 
  Approximately 35 years of experience in data engineering, database development, business intelligence, analytics engineering, or a related role involving hands-on ownership of data pipelines and databases.
• Strong Microsoft SQL Server and T-SQL skills, including experience developing and tuning complex queries, stored procedures, and data transformations.
  
• Hands-on experience developing,
  maintaining, or troubleshooting SSIS packages and scheduled ETL workflows.
• Experience with relational databases, data warehouses, dimensional modeling, and structured data integration.
  
• Strong analytical skills and the ability to translate business questions and operational processes into data requirements and technical solutions.
  
• Experience validating data through reconciliation, profiling, exception analysis, and repeatable testing.
  
• Understanding of
  ETL and ELT concepts, incremental processing, dependencies,restartability, error handling, and production support practices.
• Ability to independently investigate ambiguous data issues,
  identifyroot causes, assess business impact, and communicate conclusions.
• Experience using Git or another source-control platform and familiarity with code review and controlled deployment practices.
  
• Strong attention to detail, particularly when working with financial, transactional, customer-facing, or operational data.
  
• Clear written and verbal communication and the ability to collaborate with technical and nontechnical stakeholders.
  
• Bachelors degree in computer science, information systems, data science, engineering, mathematics, finance, or
  a relatedfield, or equivalent practical experience.

Helpful, But Not Required

• Experience with
  logistics, shipping, supply chain, transportation, carriers, couriers, invoicing, billing, revenue, or other financial systems.
• Experience with PostgreSQL or another open-source relational database.
  
• Exposure to Snowflake,
  dbt, cloud data platforms, or modern ELT architectures. Deep Snowflake expertise is notrequired.
• Experience using Python for data processing, automation, analysis, testing, or pipeline development.
  
• Familiarity with statistics, forecasting, anomaly detection, machine learning, or other applied data-science methods.
  
• Experience with Microsoft Azure, Azure DevOps, Jira, CI/CD, data visualization tools, or supporting a SaaS platform in healthcare or another regulated environment.
  

HowYoullGrow

• Build a deep understanding of VPLs operational,
  logistics, billing, and analytical data domains.
• Progress from supporting existing SQL Server and SSIS processes to designing and implementing modern PostgreSQL and Snowflake solutions.
  
• Develop stronger capabilities in data architecture, ELT, automated data quality, observability, Python, and analytics engineering.
  
• Expand the use of statistical and data-science techniques to
  identifyrisk, explain operational outcomes, and create new businessinsight.
• Grow toward advanced data engineering, analytics engineering, data architecture, or applied data-science responsibilities.
  

Current and Target Technology

• Microsoft SQL Server, T-SQL, SQL Server Integration Services (SSIS), SQL Server Agent, and a SQL Server-based enterprise data warehouse.
  
• PostgreSQL and Snowflake as VPLs target data platforms.
  
• ETL and ELT pipelines, dimensional data models, relational models, data-quality controls, and analytical datasets.
  
• Microsoft Azure, Azure DevOps, Git, Jira, and automated build and deployment practices.
  
• Python, data visualization, statistical analysis, and modern data engineering tools as the platform evolves.
  

Whats In ItForYou

• Own data processes that directly affect VPLs customers, operations, financial performance, and business decisions.
  
• Develop modern data engineering skills while helping guide a meaningful transition from SQL Server and SSIS to PostgreSQL and Snowflake.
  
• Work across engineering, analytics, product, finance, and operations to solve complex, high-value data problems.
  
• Join a low-bureaucracy, fast-paced environment where improvements to data quality, reliability, and insight have
  visiblebusiness impact.