Data Engineer

Description 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.

• 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. Requirements * 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. Technology Doesn't Change the World, People Do.®, All applicants applying for U.S. job openings must be legally authorized to work in the United States. Benefits are available to contract/temporary professionals, including medical, vision, dental, and life and disability insurance. Hired contract/temporary professionals are also eligible to enroll in our company 401(k) plan. Visit roberthalf.gobenefits.net for more information.

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