Senior Manager, Data Engineering

SRS Distribution•McKinney, TX

3d•Hybrid

About The Position

The Sr Data Engineering Manager is responsible for defining and executing the enterprise data engineering strategy, ensuring scalable, secure, and business-aligned data platforms that enable advanced analytics, AI, and digital transformation initiatives. This role provides strategic leadership across data architecture, platform engineering, data governance, and engineering operations while building and developing high-performing teams. As a senior leader, this position partners closely with executive leadership, product organizations, business stakeholders, enterprise architecture, cybersecurity, and analytics teams to establish a modern data ecosystem that accelerates business outcomes. The Sr Data Engineering Manager drives technology roadmaps, investment decisions, operational excellence, and organizational capability development while ensuring data platforms remain scalable, reliable, and future-ready.

Requirements

Minimum of 10 years of experience in Data Engineering or equivalent, demonstrated through work experience, academic training, military service, or education.
At least 5 years of proven management experience, showcasing the ability to lead and develop high-performing teams.
A minimum of 5 years of hands-on experience in data warehousing, specifically utilizing Snowflake.
Comprehensive understanding of systems architecture and design principles to effectively develop and implement data solutions.
Adept at building in-depth subject matter knowledge in individual data domains, with the capacity to guide and mentor development teams.
Proficient in development languages such as Python, SQL, Scala, or Java.
Experienced in real-time data and streaming application development, with at least 2 years of direct exposure.
Demonstrated experience with at least one public cloud platform, such as AWS, Microsoft Azure, or Google Cloud, for a minimum of 3 years.
Familiarity with data ecosystem tools for real-time batch data ingestion, ETL processing, and reporting.
Background in the supply chain or distribution industry, providing valuable insights and domain-specific strategies.
Exposure to multiple platform stacks and tools including but not limited to Databricks, Matillion, DBT, and Airflow Orchestration.
Experience in cloud data engineering and migration processes, enhancing organizational data capabilities.
Strong ability to collaborate with digital product managers and various stakeholders to deliver robust, cloud-based data solutions.
Proven experience in gathering technical and business requirements and effectively communicating them to internal and external partners.
Ability to manage and influence internal partners associated with the data technology function to achieve strategic objectives.
Experience in identifying opportunities for data engineering process improvements and developing risk control measures.
Proficient in managing and supporting risks encountered by the engineering team and driving continuous improvement efforts.
Minimum of 8 years of data engineering experience or its equivalent, which can be demonstrated through any combination of work experience, training, military service, or education.
Accumulated at least 3 years of management experience.
Accumulated at least 3 years in data warehousing, specifically with Snowflake.
Bachelor's degree in Computer Science, Information Technology, or a related technical field from an accredited institution.

Nice To Haves

Advanced experience in development using modern programming languages such as Python, SQL, Scala, or Java, with a minimum of 5 years leading development projects in these or similar languages.
Demonstrated proficiency in working with real-time data processing and streaming applications, with at least 2 years of hands-on experience in this area.
Extensive experience, over a minimum of 3 years, in leveraging public cloud services, such as AWS, Microsoft Azure, or Google Cloud, to architect and deploy scalable data solutions.
Familiarity with supply chain or distribution industry practices, which would facilitate a deeper understanding of the SRS Distribution business model and objectives.
Competency in handling and integrating multiple platform stacks and tools, such as Databricks, Matillion, DBT, and Airflow Orchestration, to enhance data processing workflows.
Proven expertise in conceptualizing and designing systems architecture specifically tailored for data-centric solutions.
Experience with data ecosystem tools, emphasizing real-time batch data ingestion, ETL processes, and effective reporting solutions.
A strong background in cloud data engineering and a history of successful data migrations, underscoring the ability to transition legacy systems into modern cloud-based architectures.
Prior experience in technical roles within the distribution industry, with a focus on optimizing data management processes and systems.
Demonstrated ability to lead technical teams, with a focus on promoting best practices in data modeling and establishing governance standards across the data environment.
Strong business acumen with proven experience in translating complex business requirements into scalable and effective technical solutions, fostering cross-functional collaboration and alignment.
Master's degree in Computer Science, Data Engineering, Information Technology, or a related technical field is highly desirable.
Certified Data Management Professional (CDMP)
AWS Certified Data Analytics Specialty
Google Professional Data Engineer Certification
Microsoft Certified: Azure Data Engineer Associate
Snowflake SnowPro Core Certification

Responsibilities

Define and execute the multi-year enterprise data engineering roadmap aligned with business strategy.
Establish standards, frameworks, and governance practices for data architecture, engineering, and platform operations, particularly Snowflake and SQL Server, to enhance data infrastructure scalability and efficiency.
Own data engineering budgets, cloud spend optimization, vendor relationships, and technology investment planning.
Drive modernization initiatives including cloud migration, real-time analytics, AI/ML enablement, and self-service data capabilities.
Lead multiple engineering team members while developing succession plans and leadership pipelines. Establish engineering operating models, performance metrics, career development frameworks, and workforce planning strategies. Foster a culture of innovation, accountability, continuous learning, and operational excellence.
Establish architectural standards for cloud data platforms, data products, integration frameworks, metadata management, and data quality. Lead architecture reviews and ensure compliance with security, governance, and regulatory requirements.
Partner with product managers, enterprise teams, and technical teams to standardize and govern data products across the ecosystem, ensuring alignment with organizational goals and data governance policies.
Oversee the allocation of team resources and financial assets to ensure successful completion of data engineering projects, aligned with strategic business objectives.
Develop and integrate cutting-edge data environments with emerging technologies, streamline processes, and facilitate seamless integration with organizational systems.
Conduct rigorous unit testing and peer reviews to ensure high-quality, efficient, and scalable code, maximizing performance and minimizing risk.
Engage directly with business stakeholders to gather requirements and deliver cloud-based, customer-focused solutions that enhance user experiences and meet business needs.
Identify process improvement opportunities within the data engineering function, implement risk control measures, and manage escalations to foster continuous improvement and innovation.
Manage agile ceremonies, including daily scrums, backlog grooming, sprint planning, and retrospectives, to maintain team alignment, productivity, and agile best practices.