Position Summary The Data Engineer designs, builds, and maintains scalable data solutions that support operational reporting, analytics, and enterprise decision-making. This role is responsible for developing reliable data pipelines, integrating data from internal and third-party systems, and delivering trusted, well-structured data for business use across the organization. The position combines hands-on engineering, data architecture, and operational support. The Data Engineer will design ETL/ELT workflows, manage lakehouse, warehouse, and database assets, optimize performance, enforce data quality standards, and support secure, governed access to enterprise data. This role partners closely with application owners, analysts, developers, and IT leadership to translate business needs into sustainable technical solutions. A successful candidate will bring strong SQL and data modeling skills, experience with cloud and hybrid data platforms, and practical knowledge of orchestration, automation, and monitoring. Experience with Microsoft Fabric, relational databases, APIs, file-based ingestion, and modern data engineering practices is highly valued. This position requires the ability to manage multiple priorities, contribute to project delivery and operational support, document solutions clearly, and participate in maintenance or incident response activities when needed. The ideal candidate is collaborative, detail-oriented, and committed to building resilient, efficient, and secure data solutions. Essential Duties and Responsibilities Design, build, and maintain scalable data pipelines and data integration processes that move data reliably from source systems into curated analytical and operational data stores. Develop ETL/ELT workflows for structured, semi-structured, and file-based data using appropriate orchestration, transformation, and scheduling methods. Implement and support Microsoft Fabric solutions, including Data Factory pipelines, Lakehouse, Warehouse, Dataflow Gen2, notebooks, and related services for ingestion, transformation, and delivery of trusted data. Design and maintain data models, schemas, and data structures that support reporting, analytics, and downstream application needs. Integrate data from enterprise applications, databases, APIs, flat files, and third-party platforms while ensuring data accuracy, completeness, and consistency. Optimize data queries, transformation logic, storage design, and pipeline performance to improve reliability, scalability, and cost efficiency. Implement monitoring, alerting, logging, and data quality checks to detect failures, anomalies, and processing issues before they affect business operations. Support data governance, security, and compliance requirements by applying access controls, audit practices, retention standards, and secure data handling procedures. Collaborate with business stakeholders, analysts, developers, and IT team members to gather requirements, define technical approaches, and deliver high-value data solutions. Create and maintain technical documentation for pipelines, source mappings, transformations, data definitions, standards, and operational procedures. Participate in troubleshooting, root cause analysis, and continuous improvement efforts related to data platform health, performance, and service reliability. Contribute to standards for version control, testing, deployment, and change management for data assets and engineering workflows. Perform additional duties as assigned in support of evolving business priorities and enterprise data initiatives. Essential Duties and Responsibilities Design, build, and maintain scalable data pipelines and data integration processes that move data reliably from source systems into curated analytical and operational data stores. Develop ETL/ELT workflows for structured, semi-structured, and file-based data using appropriate orchestration, transformation, and scheduling methods. Implement and support Microsoft Fabric solutions, including Data Factory pipelines, Lakehouse, Warehouse, Dataflow Gen2, notebooks, and related services for ingestion, transformation, and delivery of trusted data. Design and maintain data models, schemas, and data structures that support reporting, analytics, and downstream application needs. Integrate data from enterprise applications, databases, APIs, flat files, and third-party platforms while ensuring data accuracy, completeness, and consistency. Optimize data queries, transformation logic, storage design, and pipeline performance to improve reliability, scalability, and cost efficiency. Implement monitoring, alerting, logging, and data quality checks to detect failures, anomalies, and processing issues before they affect business operations. Support data governance, security, and compliance requirements by applying access controls, audit practices, retention standards, and secure data handling procedures. Collaborate with business stakeholders, analysts, developers, and IT team members to gather requirements, define technical approaches, and deliver high-value data solutions. Create and maintain technical documentation for pipelines, source mappings, transformations, data definitions, standards, and operational procedures. Participate in troubleshooting, root cause analysis, and continuous improvement efforts related to data platform health, performance, and service reliability. Contribute to standards for version control, testing, deployment, and change management for data assets and engineering workflows. Perform additional duties as assigned in support of evolving business priorities and enterprise data initiatives. Minimum Requirements and Qualifications Education and Experience Bachelor's degree in Computer Science, Information Systems, Engineering, Mathematics, or a related technical field; equivalent practical experience may be considered. Minimum of 5 years of professional experience in data engineering, ETL/ELT development, data integration, database development, or a related enterprise data role, including experience building production-grade data pipelines and supporting business-critical data platforms. Certifications Relevant Microsoft, Azure, Fabric, data, or cloud platform certifications are preferred. Certifications related to data engineering, security, analytics, or platform administration are a plus. Core Competencies Ability to design and support scalable, maintainable data pipelines and integration workflows across multiple systems. Strong understanding of data quality, lineage, governance, and secure data management practices. Proven ability to analyze requirements, solve complex technical problems, and communicate clearly with technical and non-technical stakeholders. Demonstrated ownership, attention to detail, documentation discipline, and commitment to operational reliability. Technical Expertise Candidates must demonstrate in-depth knowledge and hands-on experience with:

Data Platforms :

Microsoft Fabric, SQL Server, PostgreSQL, MySQL, cloud data services, lakehouse and warehouse platforms, or equivalent enterprise technologies

Data Engineering :

ETL/ELT design, pipeline orchestration, ingestion frameworks, transformation logic, data quality controls, and batch or scheduled processing

Development :

SQL, Python, stored procedures, views, functions, scripting, API integration, and automation

Data Modeling and Storage :

Relational design, dimensional modeling, partitioning, indexing, schema design, and storage optimization

Operations and Reliability :

Monitoring, logging, alerting, troubleshooting, backup and recovery, disaster recovery, and performance tuning

Security and Governance :

Role-based access, auditing, encryption awareness, retention practices, and support for compliance requirements Preferred Tools and Knowledge Experience with Microsoft Fabric components such as Data Factory pipelines, Lakehouse, Warehouse, Dataflow Gen2, notebooks, and semantic models. Familiarity with medallion architecture, Delta-based storage patterns, dimensional models, and modern analytics delivery practices. Experience integrating data from REST APIs, flat files, ERP or line-of-business applications, SaaS platforms, and external data providers. Knowledge of source control, release management, and CI/CD practices for data engineering assets and platform changes. Exposure to Spark, data warehouse optimization, business intelligence platforms, and enterprise data governance tools is beneficial.

Pay:

$110,000.00 - $125,000.00 per year

Benefits:

401(k) 401(k) matching Dental insurance Health insurance Life insurance Paid time off Tuition reimbursement Vision insurance People with a criminal record are encouraged to apply

Work Location:

In person