Data Engineering Lead

Huntington National Bank•Columbus, OH

3d•Onsite

About The Position

The Data Engineering Lead builds and operationalizes data for enterprise data and analytics initiatives using industry-standard practices and tools. The role leads the development, management, optimization, and production deployment of data pipelines for analysts, data scientists, and other enterprise data consumers. It also tests data quality to ensure that data conforms to business rules and is accurate, complete, consistent, and uniform. The position serves as a technical leader and develops current team members. The lead partners with data science teams, business analysts, stakeholders, and data governance teams to refine requirements, support data consumption, and promote governed reuse of curated data. The role also helps modernize data management infrastructure, trains colleagues in data pipelining and preparation, and provides leadership, coaching, and mentoring. It monitors technology trends and helps teams adapt to change while ensuring services meet business needs.

Requirements

Bachelor's degree in computer science, statistics, a related field, or relevant work experience.
At least 7 years of related experience in data management disciplines, including data integration, modeling, optimization, and data quality, or other areas directly relevant to data engineering responsibilities.
At least 7 years of experience with database programming languages and advanced analytics.
Ability to communicate complex results to technical and non-technical audiences.
Ability to work effectively both in teams and independently across multiple tasks while meeting aggressive timelines.
Strategic, intellectually curious approach with a focus on outcomes.
Professional image and ability to form relationships across functions.

Nice To Haves

Master's degree in computer science, statistics, or a related field.
Hands-on data testing experience.
Demonstrated success working with data preparation tools.
Extensive experience with data engineering tools, such as Glue, Landa, Athena, and AWS.
Expert knowledge of business intelligence software tools.
Expert experience with open-source and commercial data science platforms.
Experience with Lean and/or Agile methodology.
Strong experience with data management architectures and processes.
Strong ability to design, build, and manage data pipelines.
Strong experience working with large, heterogeneous datasets to build and optimize data pipelines, pipeline architectures, and integrated datasets.
Demonstrated success extracting business value from large, heterogeneous datasets.
Strong experience with DevOps capabilities such as version control, automated builds, testing, and release management.
Ability to train more junior analysts on day-to-day activities as necessary.
Proven ability to lead cross-functional teams.
Willingness and ability to learn new technologies on the job.
Financial services background.

Responsibilities

Architect, create, manage, maintain, and optimize data pipelines, and move them into production.
Help renovate data management infrastructure to automate data integration and management.
Partner with data science teams and business analysts to refine data requirements and consumption needs for data and analytics initiatives.
Train colleagues across the organization in data pipelining and preparation techniques.
Work with data governance teams and help vet and promote business- and data-science-created content to the curated data catalog for governed reuse.
Provide leadership, coaching, and mentoring to team members, and develop the team to work across the organization.
Work with stakeholders to clarify business needs and ensure services meet those needs.
Anticipate and analyze technology trends and assess the impact of emerging technologies.
Coach individuals through change and serve as a role model.
Perform other duties as assigned.