Consultant, Data Engineer (Tech Lead)

Nationwide•Columbus, OH

13h•$118,000 - $222,000•Hybrid

About The Position

If you’re passionate about being part of a dynamic organization that enables a Fortune 100 company with nearly $70 billion in annual sales to drive innovation and adopt new technologies that deliver business results, then Nationwide’s Technology team could be the place for you! At Nationwide®, “on your side” goes beyond just words. Our customers are at the center of everything we do and we’re looking for associates who are passionate about delivering extraordinary care. This role will work a hybrid schedule coming into the Columbus, Ohio office 2 days (Wednesday & Thursday) per week. This role does not qualify for employer-sponsored work authorization. Nationwide does not participate in the Stem OPT Extension program. Be at the forefront of building our new cloud-native Annuity Data Platform. If you’re passionate about modern data platforms and leading strong data engineers, we invite you to apply for this role in the Annuity Modernization program. 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

Deep experience writing, tuning, and optimizing complex SQL across multiple RDBMS systems.
Strong troubleshooting skills with the ability to solve complex data pipeline and performance issues.
Excellent communication, collaboration, and problem‑solving skills.
Undergraduate studies in computer science, management information systems, business, statistics, math, a related field or comparable experience and education strongly preferred.
Five to eight years of relevant experience with data quality rules, data management organization/standards and practices.
Solid experience with software development on large and/or concurrent projects.
Experience in data warehousing, statistical analysis, data models, and queries.
Advanced skills with modern programming and scripting languages (e.g., SQL, R, Python, Spark, UNIX Shell scripting, Perl, or Ruby).
Strong problem solving, oral and written communication skills.
Ability to influence, build relationships, negotiate and present to senior leaders.

Nice To Haves

Experience in financial services or insurance.
Graduate studies in business, statistics, math, computer science or a related field are a plus.
Insurance/financial services industry knowledge a plus.
Certifications are not required but encouraged.
One to three years’ experience with developing compelling stories and distinctive visualizations.

Responsibilities

Lead the design and delivery of scalable, production-grade data products across cloud and hybrid environments, with deep hands-on expertise in Snowflake, Python, and modern data engineering practices.
Provide technical leadership to a global team of 5–6 data engineers, collaborating with analysts, QA, and business partners to translate requirements into robust, high-quality data solutions.
Develop and optimize complex ETL/ELT workflows and SQL workloads across Postgres, Oracle, SQL Server, and Snowflake, ensuring reliability, performance, and maintainability at scale.
Engineer and deploy containerized data applications using Kubernetes and cloud platforms (AWS preferred), and design/manage orchestration workflows using UAC or similar tools.
Drive data quality, lineage, observability, and engineering best practices, contributing to and enforcing standards, frameworks, and repeatable patterns across the broader engineering team.
Consults on complex data product projects by analyzing moderate to complex end to end data product requirements and existing business processes to lead in the design, development and implementation of data products.
Responsible for producing 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.
Translates business data stories into a technical story breakdown structure and work estimate so value and fit for a schedule or sprint.
Responsible for applying secure software and systems engineering practices throughout the delivery lifecycle to ensure our data and technology solutions are protected from threats and vulnerabilities.
Creates 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.
Builds data cleansing, imputation, and common data meaning and standardization routines from source systems by understanding business and source system data practices and by using data profiling and source data change monitoring, extraction, ingestion and curation data flows.
Facilitates medium to large-scale data using cloud technologies – Azure and AWS (i.e. Redshift, S3, EC2, Data-pipeline and other big data technologies).
Collaborates with 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.
Participates as an expert and learner in team tasks for data analysis, architecture, application design, coding, and testing practices.
May perform other responsibilities as assigned.

Benefits

medical/dental/vision
life insurance
short and long term disability coverage
paid time off with newly hired associates receiving a minimum of 18 days paid time off each full calendar year pro-rated quarterly based on hire date
nine paid holidays
8 hours of Lifetime paid time off
8 hours of Unity Day paid time off
401(k) with company match
company-paid pension plan
business casual attire
Nationwide is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive culture where everyone feels challenged, appreciated, respected and engaged. Nationwide prohibits discrimination and harassment and affords equal employment opportunities to employees and applicants without regard to any characteristic (or classification) protected by applicable law.