Data EngineerLocation: Miamisburg, OH (HQ)
Department: Information Technology
Reports To: AI & Data Solutions Manager
FLSA Status: Full-Time, Exempt
Level: IC
Travel: Minimal
SummaryReporting to the AI & Data Solutions Manager, the Data Engineer will design, build, and maintain scalable data solutions that support reporting, analytics, data governance, and informed decision-making across Covation Global / United Wheels Inc. This role is responsible for developing efficient data pipelines, data models, integrations, and visualization-ready datasets while ensuring data quality, security, reliability, and performance. The Data Engineer will partner closely with Business Intelligence Analyst(s), the Data Governance Team, and IT applications, operations, and web development teams to modernize the company's data platform and establish consistent development standards, documentation, and data lineage practices. Experience with AWS data services is strongly preferred and will support the organization's continued cloud data modernization efforts.
What Success Looks Like (3 Core Outcomes):- Reliable, Scalable Data Pipelines: Data pipelines and integrations run reliably and efficiently, extracting, transforming, and loading data from internal and external sources with strong data quality, security, and performance.
- Analytics-Ready Data Platform: Well-designed data models, semantic layers, and curated datasets support reporting, dashboards, and self-service analytics, advancing the company's cloud data modernization on AWS.
- Well-Governed, Well-Documented Data: Data quality metrics, validation routines, design documentation, and data lineage practices are maintained, keeping the platform accurate, secure, and aligned with governance and change-management standards.
Essential Duties and Responsibilities: Other duties may be assigned.
Data Pipelines, Modeling & Integration- Model, design, develop, test, and implement backend data structures, reporting datasets, and front-end data solutions to meet business visualization and reporting requirements.
- Design, develop, test, and maintain data pipelines for efficient extraction, transformation, and loading from internal and external data sources.
- Build aggregate data models, dimension views, fact tables, semantic layers, and curated datasets that support analytics and self-service reporting.
- Develop and maintain data integration solutions using SQL, Python, ETL/ELT tools, APIs, and cloud-native data services.
- Support AWS-based data platform capabilities, including storage, transformation, orchestration, compute, and analytics services such as Amazon S3, AWS Glue, Amazon Redshift, AWS Lambda, Amazon RDS, Athena, and related services.
Reliability, Quality & Operations- Maintain the integrity, reliability, security, and performance of company databases and data pipelines.
- Monitor production jobs, provide support for data pipeline failures, remediate issues, and automate routine operational processes.
- Develop data quality metrics, validation routines, and quality control tests to verify data accuracy, completeness, and consistency.
- Monitor key performance indicators and recovery time objectives to support service level agreements and maximize business value.
- Analyze data integration problems, recommend corrective actions, and develop improved processes to meet service levels and business needs.
Documentation, Governance & Standards- Create and maintain design documentation for data integration, data modeling, reporting, and data lineage projects.
- Coordinate with the Business Intelligence Analyst(s), Data Governance Team, and IT teams to align with change management, security, and governance standards.
- Act as a technical resource for system and application design, performance optimization, integration, and data security considerations.
- Follow industry best practices for development standards, code management, documentation, peer review, and knowledge transfer.
- Perform other related duties as assigned to support the team's function and broader business objectives.
Supervisory Responsibilities: This role does not include supervisory responsibilities.
Education and/or Experience- Bachelor's degree in Data Engineering, Computer Science, Information Systems, Data Science, Engineering, or a related field; master's degree or graduate-level coursework related to data engineering, analytics, or data science preferred.
- 3+ years of experience designing, developing, and supporting data pipelines, analytical queries, reports, data models, and visualization-ready datasets.
- Experience designing and developing reports, dashboards, or visualizations using Amazon Quick, Power BI, Tableau, or similar reporting tools.
- Hands-on experience with AWS services such as Amazon S3, AWS Glue, Amazon Redshift, AWS Lambda, Amazon RDS, Athena, CloudWatch, IAM, and related cloud data services is strongly preferred.
- Proficiency in SQL and at least one programming or scripting language such as Python, Java, C#, Scala, or R.
- Solid understanding of data engineering principles, relational and dimensional modeling, data architecture, ETL/ELT design, metadata management, and data lineage.
- Demonstrated experience developing solutions for relational databases and cloud data warehouses such as Snowflake, BigQuery, Amazon Redshift, Azure SQL Database, SQL Server, or similar platforms.
- Familiarity with API-first architectures, REST APIs, data integration patterns, and secure data exchange practices.
- AWS Certified Data Engineer - Associate, AWS Certified Solutions Architect - Associate, or similar cloud/data certification preferred.
Required Skills and Qualifications- Strong SQL development skills, including complex queries, stored procedures, views, performance tuning, and data validation.
- Experience building and maintaining ETL/ELT workflows, data pipelines, and scheduled data processing jobs.
- Ability to design scalable data models that support reporting, analytics, and cross-functional business decision-making.
- Working knowledge of data governance concepts, including data quality, data lineage, documentation, privacy, and access control.
- Experience troubleshooting data pipeline failures, remediating production issues, and improving operational reliability.
- Ability to translate business requirements into technical specifications and communicate technical concepts to non-technical stakeholders.
- Strong analytical and problem-solving skills with attention to detail and accuracy.
- Demonstrated ability to work collaboratively with business stakeholders, analysts, developers, and IT teams.
- Familiarity with software development lifecycle, agile methodologies, version control, and structured change management practices.
Preferred Qualifications- Hands-on AWS data engineering experience, including building data lakes or lakehouse-style architectures using Amazon S3, AWS Glue, Amazon Redshift, Athena, Lambda, and related services.
- Experience with AWS Glue Data Catalog, crawlers, jobs, workflows, and data cataloging practices.
- Experience designing secure cloud data solutions using IAM roles, encryption, secrets management, networking considerations, and least-privilege access patterns.
- Experience with DevOps or DataOps practices in cloud environments, including CI/CD, infrastructure as code, automated testing, monitoring, and deployment pipelines.
- Experience using Amazon Quick or AWS-native analytics tools to enable reporting and self-service analytics.
- Familiarity with event-driven or serverless data architectures using AWS Lambda, EventBridge, Step Functions, or similar technologies.
- Experience with modern data warehouse or data lake platforms such as Snowflake, Databricks, Redshift, BigQuery, or Microsoft Fabric.
- Knowledge of API integrations, EDI concepts, ERP data, manufacturing, supply chain, finance, sales, or retail analytics data domains is a plus.
Language Skills- Ability to translate business requirements into technical specifications and to communicate technical concepts clearly to both technical and non-technical stakeholders.
- Ability to create clear design documentation, data lineage records, and knowledge-transfer materials.
Mathematical Skills- Strong quantitative and analytical reasoning to support data modeling, validation, performance tuning, and data quality analysis.
Reasoning Ability- Ability to analyze complex data integration and pipeline problems, identify root causes, and design scalable, reliable solutions.
Computer Skills- Proficiency in SQL and at least one programming/scripting language (e.g., Python, Java, C#, Scala, or R).
- Hands-on experience with AWS data services (e.g., S3, Glue, Redshift, Lambda, RDS, Athena) strongly preferred; experience with BI tools such as Amazon Quick, Power BI, or Tableau.
- Proficiency in Microsoft Office Suite.
