Data Engineer

We are looking for a Data Engineer to lead the design and adoption of a scalable data quality framework built on Great Expectations across enterprise data environments in Cincinnati, Ohio. This Long-term Contract position will focus on strengthening trust in data used for reporting, analytics, and operational decision-making by embedding quality controls into modern pipelines and cloud-based platforms. The role works closely with engineering, governance, analytics, and business teams to establish practical standards, automate validation processes, and improve visibility into data health across the organization.

Responsibilities:
• Shape and standardize the organization’s approach to data quality by creating reusable Great Expectations assets, test patterns, and validation frameworks.
• Implement and refine Great Expectations across batch, streaming, and cloud-based data workflows to support dependable quality checks throughout the data lifecycle.
• Administer core GX components, including Data Contexts, Expectation Suites, Checkpoints, and generated documentation, to ensure consistent execution and maintainability.
• Define and promote effective methods for writing expectations, running validations, and documenting outcomes for technical and business audiences.
• Establish measurable data quality indicators such as completeness, accuracy, validity, and timeliness, and build processes to track performance over time.
• Integrate automated validation into engineering delivery pipelines using tools such as GitHub Actions, Azure DevOps, or Jenkins to support repeatable deployment practices.
• Investigate recurring quality defects, identify underlying causes, and coordinate corrective actions with engineering and business stakeholders.
• Create dashboards and reporting solutions in tools such as Power BI or Databricks to communicate trends, exceptions, and risk areas.
• Embed data validation into platforms such as Azure Data Factory, Databricks, Delta Live Tables, Airflow, or similar orchestration environments across development, test, and production stages.
• Guide engineers, analysts, data stewards, and quality resources by providing training, support, and leadership on how to build, maintain, and use Great Expectations validations effectively.

Qualifications:

• Bachelor’s degree in Computer Science, Information Systems, Data Engineering, or a related discipline.
• Demonstrated experience implementing and supporting Great Expectations in enterprise data environments.
• Strong programming capability in Python with practical use of technologies such as pandas and Apache Spark.
• Hands-on experience with data engineering platforms and databases, including tools such as Databricks, PostgreSQL, or comparable technologies.
• Knowledge of cloud-based data validation practices in environments such as Azure and other modern data ecosystems.
• Solid understanding of ETL and ELT design patterns, orchestration concepts, and data modeling approaches.
• Ability to define data quality metrics and deploy automated controls at scale across complex pipelines.
• Familiarity with big data and streaming technologies such as Apache Hadoop or Apache Kafka is preferred.

Compensation

$80.75-$93.50 Hourly

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