Data Engineer
Summary
Data Engineer at Covation Global / United Wheels (on-site in Miamisburg, OH) builds and maintains data pipelines, models, and integrations that power reporting, analytics, and data governance. Day-to-day work centers on SQL and Python ETL/ELT development, AWS data services (S3, Glue, Redshift, Lambda, Athena), BI support, and data quality/lineage documentation.
Department: Information Technology
Reports To: AI & Data Solutions Manager
Level: IC
Travel: Minimal
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- Integrity: We do what we say. Operates with honesty, transparency, and accountability at every step; honors commitments to partners, riders, and colleagues, and builds trust through consistent, responsible decisions.
- Simplicity: Complexity is a tax. Delivers clarity, ease, and purposeful design in everything; streamlines processes, removes unnecessary steps, and focuses on what matters most.
- Priority: Focus on what matters most. Ruthlessly prioritizes the critical few over the trivial many, concentrating energy and resources where they drive the greatest value and outcome.
- Agility: Speed is precision in motion. Moves with purpose, speed, and adaptability; embraces change, thinks creatively, acts decisively, and responds quickly to shifting needs and market conditions.
- Rider Centricity: Keeps the rider at the center of decisions. Anticipates needs and works to deliver best-in-class experience, better quality, and better value across every brand and function.
- Collaborative Innovation: Develops game-changing solutions inspired by the rider, supported by collective research and shared expertise, and validated by business results; challenges the status quo to drive improvement.
- Operational Excellence: Delivers results with lean, efficient execution. Drives continuous improvement across products, processes, operations, costs, and service, while maximizing value for the organization.
- ESG & Responsible Growth: Supports sustainable innovation, ethical operations, and positive community impact, ensuring growth is achieved responsibly and with integrity to people and the planet.
- 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.
- Strong quantitative and analytical reasoning to support data modeling, validation, performance tuning, and data quality analysis.
- Ability to analyze complex data integration and pipeline problems, identify root causes, and design scalable, reliable solutions.
- 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.
Skills
- Agile
- AI
- Analytics
- API
- Athena
- AWS
- Aws Glue
- Azure
- BigQuery
- CI/CD
- Cloud
- Cloud Native
- CloudWatch
- Corrective Action
- C#
- Data Engineering
- Data Governance
- Data Lake
- Data Lineage
- Data Modeling
- Data Pipelines
- Data Quality
- Data Science
- Data Warehousing
- Databricks
- DevOps
- Dimensional Modeling
- EDI
- ELT
- ERP
- ETL
- Event Driven Architecture
- Eventbridge
- IAM
- Infrastructure as Code
- Java
- Lakehouse
- Lambda
- Metadata Management
- Microsoft Fabric
- Networking
- Power BI
- Python
- RDS
- Redshift
- REST
- S3
- Scala
- Secrets Management
- Serverless
- Snowflake
- SQL
- SQL Server
- Tableau
- Test Automation
- Version Control