VPL is looking for a hands-on Data Engineer who can combine strong engineering fundamentals with analytical problem-solving. You will help keep our current Microsoft SQL Server and SSIS data environment reliable while developing the skills and platform knowledge needed to support VPL's 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, optimize SQL, reconcile operational or financial data, and work with business partners to turn complex questions into trusted data solutions. Extensive Snowflake experience is not required; we value strong SQL, data engineering, and analytical capabilities with the ability and desire to grow into Snowflake and modern ELT practices.
What You'll Do
- Operate, monitor, troubleshoot, and improve SSIS packages and scheduled ETL jobs that support VPL's 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
maintain data pipelines that integrate operational, logistics, customer, carrier, supplier, billing, and financial data. - Develop and
maintain dimensional models, relational models, reporting datasets, and other analytical data structures that are accurate, 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
identify missing, 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,
identify patterns, 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
assist with audit or customer-assurance evidence related to data processing. - Participate in after-hours
support for production-critical data processes after completing onboarding and demonstrating readiness.
What You'll Bring
- Approximately 3-5 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,
identify root 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.
- Bachelor's degree in computer science, information systems, data science, engineering, mathematics, finance, or
a related field, 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 not required. - 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.
How You'll Grow
- Build a deep understanding of VPL's 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
identify risk, explain operational outcomes, and create new business insight. - 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 VPL's 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.
What's In It For You
- Own data processes that directly affect VPL's 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
visible business impact.
