Specialist, Data Engineer Test Lead - ETL, Python, Perl

Nationwide•Columbus, OH

11h•$95,500 - $177,500•Hybrid

About The Position

The F Band Specialist, Test Lead for the NF Actuarial Datamart is responsible for leading test planning, test execution, and data validation activities that support delivery of high-quality data and application solutions. This role partners closely with developers, analysts, and business partners to define effective testing scenarios, analyze data issues, manage defects, and respond to walk-up support requests. This position is a strong fit for someone with a solid testing background who also has an interest in development and automation. The ideal candidate brings hands-on experience in data-focused testing, enjoys problem solving, and is comfortable working across both quality assurance and technical solution delivery. It is our intention to fill this role in Columbus, Ohio on a hybrid basis. The team come in for collaboration on Mondays and Tuesdays. However, on occasion, applications from internal candidates working remotely who bring critical industry skills and relevant experience may be considered. Nationwide’s industry leading workforce is passionate about creating data solutions that are secure, reliable and efficient in support of our mission to provide extraordinary care. Nationwide embraces an agile work environment and collaborative culture through the understanding of business processes, relationship entities and requirements using data analysis, quality, visualization, governance, engineering, robotic process automation, and machine learning to produce targeted data solutions. If you have the drive and desire to be part of a future forward data enabled culture, we want to hear from you. As a Data Engineer you’ll be responsible for acquiring, curating, and publishing data for analytical or operational uses. Data should be in a ready-to-use form that creates a single version of the truth across all data consumers, including business users, data scientists, and Technology. Ready-to-use data can be for both real time and batch data processes and may include unstructured data. Successful data engineers have the skills typically required for the full lifecycle software engineering development from translating requirements into design, development, testing, deployment, and production maintenance tasks. You’ll have the opportunity to work with various technologies from big data, relational and SQL databases, unstructured data technology, and programming languages.

Requirements

5+ years of experience in testing or quality assurance, with emphasis on data validation and system/integration testing
Experience with Ruby automation or automated testing frameworks
Experience working with Informatica processes in an ETL or data integration environment
Strong experience with data validation and analysis of upstream and downstream data issues
Ability to write SQL queries for testing, troubleshooting, and validation purposes
Exposure to Python and interest in expanding development-oriented technical skills
Experience managing defects and supporting issue triage through resolution
Ability to work directly with developers to clarify requirements, define test scenarios, and improve solution quality
Strong analytical, organizational, and communication skills
Undergraduate studies in computer science, management information systems, business, statistics, math, a related field or comparable experience and education strongly preferred.
Graduate studies in business, statistics, math, computer science or a related field are a plus.
Three to five years of relevant experience with data quality rules, data management organization/standards, practices and software development.
Experience in data warehousing, statistical analysis, data models, and queries.
One to three years’ experience with Cloud technology and infrastructure including security and access management.
Moderate to advanced skills with modern programming and scripting languages (e.g., SQL, R, Python, Spark, UNIX Shell scripting, Perl, or Ruby).
Good problem solving, oral and written communication skills.

Nice To Haves

Experience supporting enterprise data platforms or actuarial data environments
Experience in ETL, data warehouse, or datamart testing
Familiarity with release validation, regression testing, and automation-first quality practices
Interest in growing from a testing-focused role into broader technical or development responsibilities
Insurance/financial services industry knowledge a plus.
Certifications are not required but encouraged.

Responsibilities

Lead testing efforts for NF Actuarial Datamart initiatives, including test planning, scenario development, execution, and validation
Assist developers in identifying and refining testing scenarios for new development, enhancements, and defect fixes
Perform detailed data validation to confirm expected results across source, transformation, and target layers
Write and execute SQL queries to investigate data conditions, validate results, and support root cause analysis
Analyze data issues and partner with development teams to determine cause, impact, and resolution approach
Manage defects through the full lifecycle, including documentation, prioritization, retesting, and closure support
Handle walk-up requests and provide timely support for testing questions, data concerns, and issue triage
Support and enhance test automation efforts, including Ruby-based automation where applicable
Work with Informatica-based processes and data movement workflows as part of validation and issue analysis
Contribute to continuous improvement of testing practices, automation coverage, and overall quality processes
Collaborate across teams to ensure quality standards are met and releases are production ready
Provides basic to moderate technical consultation on data product projects by analyzing end to end data product requirements and existing business processes to lead in the design, development and implementation of data products.
Produces data building blocks, data models, and data flows for varying client demands such as dimensional data, standard and ad hoc reporting, data feeds, dashboard reporting, and data science research & exploration
Applies secure software and systems engineering practices throughout the delivery lifecycle to ensure our data and technology solutions are protected from threats and vulnerabilities.
Translates business data stories into a technical story breakdown structure and work estimate so value and fit for a schedule or sprint is determined.
Creates simple to moderate business user access methods to structured and unstructured data by such techniques such as mapping data to a common data model, NLP, transforming data as necessary to satisfy business rules, AI, statistical computations and validation of data content.
Assists the enterprise DevSecOps team and other internal organizations on CI/CD best practices experience using JIRA, Jenkins, Confluence etc.
Implements production processes and systems to monitor data quality, ensuring production data is always accurate and available for key stakeholders and business processes that depend on it.
Develops and maintains scalable data pipelines for both streaming and batch requirements and builds out new API integrations to support continuing increases in data volume and complexity.
Writes and performs data unit/integration tests for data quality
With input from a business requirements/story, creates and executes testing data and scripts to validate that quality and completeness criteria are satisfied.
Can create automated testing programs and data that are re-usable for future code changes.
Practices code management and integration with engineering Git principle and practice repositories.
May perform other responsibilities as assigned.