Senior Manager, Data Engineering

BOK Financial•Richardson, TX

1d•Onsite

About The Position

Build data that matters at a bank with deep roots and bold plans. Our team is advancing a cloud, data, and AI transformation—creating an enterprise data lake, governed products, and scalable analytics. By connecting data across the bank, we help teams make smarter decisions, manage risk, and better serve clients. Our work drives stronger performance, more efficient operations, and more consistent experiences enterprise‑wide. The Senior Manager, Data Engineering provides leadership and oversight for enterprise data engineering capabilities, with responsibility for the design, development, and delivery of scalable data pipelines and data products across the organization. This role leads multiple teams or domains supporting data ingestion, transformation, data modeling, and CI/CD enablement for analytics and data platform solutions. The position operates across modern data engineering ecosystems, including data integration platforms, streaming and event driven technologies, transformation frameworks, and cloud data platforms, including but not limited to tools such as Fivetran, Confluent, dbt, Spark, Snowflake, Databricks, and similar technologies. The role partners closely with Data Architecture, Platforms, Analytics, and business leadership to define engineering standards, best practices, and enterprise delivery frameworks that ensure scalable, reliable, and efficient data solutions.

Requirements

Bachelor’s Degree in Computer Science, Information Systems, Engineering, or a related field, or an equivalent combination of education and experience.
15+ years of experience in enterprise data engineering, data integration, or cloud data ecosystems.
7+ years of leadership experience at Manager or Senior Manager level leading teams or large scale data initiatives.
Demonstrated expertise in modern data engineering, including design, development, and scaling of enterprise data platforms, pipelines, integration frameworks, and analytical data solutions across cloud-native and distributed ecosystems.
Deep knowledge of data ingestion, transformation, ETL/ELT architectures, data modeling, and scalable analytical data structures, including implementation on cloud platforms such as AWS, Azure, GCP, Snowflake, Databricks, or similar technologies.
Strong understanding of platform operations, reliability, observability, monitoring, security, identity and access management, resiliency, performance optimization, and cloud cost management.
Experience establishing engineering standards, governance practices, operating models, CI/CD processes, version control, automated testing frameworks, and delivery methodologies that support scalable and reliable engineering operations.
Proven ability to lead and develop engineering organizations, including organizational design, talent development, succession planning, performance management, and leadership through managers and senior technical leaders.
Demonstrated success evaluating, selecting, implementing, and governing enterprise data engineering technologies, tools, platforms, and third-party solutions.
Strong stakeholder management, communication, and influence skills, with the ability to collaborate across architecture, platform, security, analytics, and business functions and effectively communicate technical concepts to both technical and executive audiences.
Exceptional analytical, problem-solving, and decision-making capabilities, including root cause analysis, risk identification and mitigation, issue resolution, continuous improvement, and accountability for engineering delivery, operational excellence, and business outcomes.