Backend Data Engineer

• Design, develop, and maintain enterprise data pipelines supporting Navy and DoD environments
• Use Azure Data Factory (ADF) to build pipelines and establish connections between legacy systems, applications, databases, and data repositories
• Ingest data from new, unfamiliar, legacy, and disparate sources and determine the appropriate approach for extraction and integration
• Perform data transformation, cleansing, staging, aggregation, and preparation for downstream reporting and analytics
• Develop and maintain SQL queries and work directly with SQL databases and data repositories
• Integrate systems and move data between different platforms and repositories
• Troubleshoot data pipeline failures, connectivity issues, data quality problems, and integration issues
• Work with stakeholders and technical teams to understand data sources, dependencies, requirements, and downstream reporting needs
• Develop data solutions that can reliably feed Power BI and other enterprise BI/reporting tools
• Document data flows, pipeline architecture, integrations, transformations, and dependencies
• Support an evolving enterprise data environment while working with both modern cloud technologies and legacy systems
• Collaborate with the team’s front-end/BI-focused Data Engineer to ensure reporting requirements are supported by reliable backend data infrastructure

• 3-4 years of hands-on Data Engineering experience
• Active Secret clearance
• IAT Level II certification
• Hands-on experience developing data pipelines and ETL/data integration solutions
• Strong hands-on experience with Azure Data Factory (ADF)
• Strong SQL/SQL database experience
• Experience with enterprise data transformation, staging, and aggregation
• Experience integrating disparate, legacy, or multiple systems & moving data between them
• Ability to take an unfamiliar dataset and determine how to ingest, transform, store, stage, aggregate, and prepare it for downstream use
• Experience supporting data used by Power BI, Business Intelligence, or enterprise reporting environments
• Preferred: SSIS, Python, PowerShell, XML, Splunk, Azure/cloud data environments, data warehousing/enterprise data repositories

• Salary: $95,000-100,000 depending on experience
• Benefits: Medical, Dental, Vision, 401k Plan, Holidays, PTO, sick leave as required by law

Keywords: Power BI, Business Intelligence, BI, BI Developer, BI Engineer, Data Visualization, Dashboard Development, Reporting, Enterprise Reporting, SQL, SQL Server, Data Modeling, DAX, Power Query, M, ETL, ELT, Azure, Azure Data Factory, ADF, Data Transformation, Data Warehouse, Data Engineering, Data Integration, Azure Data Factory, ADF, Data Engineering, Data Pipelines, ETL, ELT, Data Integration, SQL, SQL Server, Data Transformation, Data Staging, Data Aggregation, Data Ingestion, Data Migration, Legacy Systems, System Integration, Data Warehouse, Data Repository, SSIS, Azure, Python, PowerShell, XML, Security, Clearance, DoD Secret, Active Secret Clearance, IAT Level II, Security+, DoD 8570, 8570, dod cleared, dod clearance, secret clearance, secret, secret cleared, secret security clearance, security clearance, security cleared, sec+, sec +, s+, security+, security +, “sec plus, secplu, securityplus, security plus, CompTIA Sec+, Comptiasecurity, cysa+, cysa +, cysa plus, cysa plus, cybersecurity +, cybersecurity+, cyber security +, cyber security plus, iat2, iatii, iattwo, “iat ii, iat two, iat 2, iat too, iat level ii, iat levelii, iat level 2, iat level two

Zachary Piper Solutions is seeking a Backend Data Engineer to support the USN. This role is 100% onsite and is open to candidates in Norfolk, VA. The Backend Data Engineer will join a small team that supports & manages government tools connected to the Navy’s enterprise network. The team is increasingly focused on the data engineering & data infrastructure supporting enterprise reporting and analytics. This position is heavily focused on the backend data engineering lifecycle connecting to new and existing data sources, integrating disparate and legacy systems, building data pipelines, transforming and cleaning data, staging and aggregating datasets, and preparing data for downstream reporting and business intelligence.