Data Engineer Lead

Old National Bank•Lake Elmo, MN

About The Position

Old National Bank has been serving clients and communities since 1834. With over $70 billion in total assets, we are a regional powerhouse deeply rooted in the communities we serve. As a trusted partner, we thrive on helping our clients achieve their goals and dreams, and we are committed to social responsibility and investing in our communities through volunteering and charitable giving. We continually seek highly motivated and talented individuals as our people are critical to our success. In return, we offer competitive compensation with our salary and incentive program, in addition to medical, dental, and vision insurance. 401K, continuing education opportunities and an employee assistance program are also included in our benefit suite. Old National also offers a variety of Impact Network Groups led by team members who are passionate about driving engagement, creating awareness of diverse backgrounds and experiences, and building inclusion across the organization. We offer a unique opportunity to join a growing, community and client-focused company that is firmly rooted in its core values. A Cloud Data Engineer Lead is a high-level professional responsible for designing, developing, and maintaining the data architecture in our organization. This role is responsible for overseeing the technical aspects of data management, including data integration, processing, storage, and retrieval. The Cloud Data Engineer Lead provides technical leadership to a team of Data Engineers in the enablement and support of our cloud data platform, creation and maintenance of data pipelines, data product development, and data governance patterns to ensure the timeliness and quality of data. The role is responsible for ensuring that data is accurate, reliable, and available to Analysts, Data Scientists, and other stakeholders within our organization. Our Data Engineering teams work in a highly collaborative environments, engage in daily standups, operate within Agile development practices, and participate in a GitOps development model in that everything is code.

Requirements

B.S. degree in a related field and 7 to 9 years professional experience OR a total of 7 plus years' experience as Senior Data Engineer, Senior Software Engineer, or equivalent.
5 plus years working in AWS Cloud (AWS Solution Architect Certification is a plus) and hybrid cloud environments.
7 plus years working with data warehouse platforms.
Coding experience in SQL and scripting languages such as Python and Bash and expert working knowledge of the Spark platform, preferably in a cloud data platform environment such as Databricks.
Knowledge of Agile and GitOps methodologies
Ability to communicate effectively, both orally and in writing, with clients and groups of employees to build and maintain positive relationships
Strong leadership and communication skills. You must be able to collaborate with other teams, effectively manage projects, and communicate technical information to non-technical stakeholders.

Nice To Haves

Experience working with large and complex data sets with multi-terabyte scale is a plus.
Banking experience / knowledge is a plus

Responsibilities

Data Engineering and Architecture Strategic Leadership Provide direction for the organization’s data engineering and architecture strategy.
Make final decisions on data infrastructure design and tool selection, balancing business needs with technical feasibility.
Collaborate on requirements gathering while owning the architectural vision and execution.
Champion data management best practices across the organization.
Engineering Execution and Technical Implementation Write production-quality code and implement scalable ETL pipelines using tools like AWS, S3, Glue, Lambda, and Databricks.
Apply business intelligence best practices such as dimensional modeling and distributed data processing.
Design, build, and maintain modern cloud-based data platforms to support enterprise data needs including integrations, analytics, and AI.
Team Development and Cross-Functional Collaboration Bootstrap and grow the data engineering team, setting standards and mentoring new members.
Translate business requirements into technical solutions, ensuring alignment with broader organizational goals.
Enable data-driven decision-making by delivering reliable analytics pipelines and insights.