Data Engineer Manager 2

Job Summary:

Leads a team of data engineers in designing, building, and maintaining scalable data pipelines and infrastructure. Collaborates with business stakeholders, IT partners, and analysts to implement data engineering solutions that support analytics and reporting needs. Translates strategic guidance into actionable engineering plans and ensures high-quality, timely delivery of data solutions. Promotes best practices in data architecture, governance, and performance optimization across projects.

Key Responsibilities:

Leads data engineering teams in developing, testing, and deploying robust data pipelines, integrations, and platforms to support enterprise analytics and reporting needs. Translates data strategy and architecture guidance into actionable engineering initiatives and project deliverables. Collaborates with data architects, analysts, and business partners to design and implement scalable, secure, and high-performance data solutions. Ensures adherence to data governance, privacy, and compliance standards in all data engineering activities. Oversees performance optimization, reliability, and maintenance of data systems and platforms. Guides team members in adopting best practices in data modeling, ETL development, and cloud data services. Monitors system capabilities, identifies improvement opportunities, and implements upgrades and enhancements. Develops and mentors data engineers to build technical proficiency and career growth within the team. Provides technical expertise and problem-solving support for complex data issues within ongoing projects. Supports operational and business decision-making by ensuring timely and accurate data availability across key systems. Responsible for team financial management including annual operating plan (AOP), budget management, and forecasting to ensure alignment with organizational goals

Core Responsibilities Unique to the Role
Lead the engineering delivery of enterprise data products by establishing scalable data platforms, reusable data assets, and AI-ready data capabilities that enable analytics, operational reporting, automation, and advanced AI use cases across Supply Chain, Quality, Finance, Product Lifecycle, and other enterprise domains.
Drive Data-as-a-Product engineering practices by partnering with Product Managers, Solution Engineering, Architecture, and business stakeholders to prioritize technical investments, define engineering roadmaps, and ensure data products are discoverable, governed, reliable, and reusable across multiple consumers.
Own engineering excellence and delivery confidence for the Power Cell by establishing standards for data quality, lineage, observability, platform performance, and operational sustainability while balancing capacity, technical debt, and business value realization.

Required Skills, Education, or Experience

10+ years of experience
Demonstrated experience leading data engineering teams responsible for enterprise data platforms, analytics solutions, data products, or modern cloud-based data ecosystems.
Strong understanding of data architecture, data modeling, data integration, ETL/ELT frameworks, cloud data platforms, APIs, and scalable data delivery patterns supporting analytics and AI consumption.
Experience translating business and product requirements into engineering execution plans, making informed decisions regarding architecture, scalability, performance, reusability, and maintainability.
Ability to lead cross-functional agile teams and collaborate effectively with Product Managers, Architects, Data Scientists, Solution Engineers, and business stakeholders to deliver measurable business outcomes.
Bachelor s degree in Computer Science, Information Technology, Engineering, Data Analytics, or equivalent practical experience.

Preferred (Nice to Have) Skills, Education, or Experience (1-2)
Experience working within a Data-as-a-Product operating model, including data catalog adoption, lineage, governance, data quality frameworks, reusable data assets, and data product lifecycle management.
Exposure to AI/ML-enabled data ecosystems, including support for feature engineering, GenAI, advanced analytics, real-time data processing, and AI-ready data platform architectures.

Competencies: Security Compliance Principles - Applies standards, tools, and best practices to embed security, privacy, and compliance into the design/build/test/operate lifecycle for products, services, apps, systems, software, and configurations-balancing protection, efficiency, and cost. Programming Principles - Applies programming languages, frameworks, and patterns to design, write, configure, test, and maintain software/solutions/systems that are efficient, secure, scalable, and reliable. Data Principles - Governs, models, secures, implements, and observes data flows to ensure integrity, quality, and compliance-enabling trusted, scalable, cost-conscious data use. Modern Development Practices - Applies modern engineering practices and tools-such as Agile/DevSecOps, CI/CD, automated testing, and infrastructure as code-to accelerate delivery, improve quality, and reduce risk across the SDLC. Solution Design - Translate business requirements into integrated designs, architectures, patterns, and system interactions that deliver customer value and align with enterprise standards and subject-matter platforms. Demonstrating Mastery - Maintains essential knowledge and proficiency in relevant domains, tools, technologies, methodologies, or frameworks through targeted credentials and rigorous proficiency, future-proofing organizational skills against strategic needs. Strategic and Innovative Thinking - Evaluates business and technology trends, anticipates future needs, develops creative approaches, and frames innovations to shape strategy and create durable value with cost-aware innovation. Technical Passion Drive - Models curiosity and excitement for technology by self-initiating continuous development, experimenting with emerging technologies, and identifying insertion opportunities that accelerate business performance. Driving Effective Outcomes - Takes ownership, acts with urgency, and initiates action to turn goals into clear plans, decisions, guardrails, and cadences while navigating ambiguity and change to drive momentum and deliver consistent results. Engaging with Impact - Communicates with clarity and purpose to align stakeholders, foster collaboration, build trust, and influence coordinated action across teams and functions to accelerate outcomes. Values Differences - Recognizing the value that different perspectives and cultures bring to an organization. Ensuring Customer Success - Embraces a customer-first mindset to deliver outcomes by linking customer needs and business priorities to aligned solutions, delivery, adoption, satisfaction, and realized value through sustained engagement that builds partnership and trust. Manages People Teams - Builds inclusive teams that empower ownership, deepen capabilities, and ensure fair, timely performance accountability, creating an environment where people can deliver their best work and top performance. Directs Work - Creates conditions for team success with clear goals, priorities, work plans, standards, effective processes, resourcing, and governance to enable fast, empowered, accountable, and quality execution. Education, Licenses, Certifications: College, university, or equivalent degree in relevant technical discipline, or relevant equivalent experience required. This position may require licensing for compliance with export controls or sanctions regulations. Experience: Experience analyzing business systems, data flows, and requirements to design and implement effective data solutions. Solid background in managing and transforming large data sets using scalable processing frameworks. Hands-on development experience with modern data tools and frameworks such as Java, Python, Spark, Hive, Kafka, SQL, and related open-source or cloud-native technologies. Experience implementing and supporting data pipelines on cloud or distributed compute environments. Familiarity with tools and methods for large-scale data movement and integration from diverse sources. Proven ability to build and optimize data models, warehouses, and analytical solutions supporting business insight. Strong understanding of database systems, performance optimization, and data management principles. Exposure to IoT or streaming data systems is a plus. Experience working in Agile or iterative software engineering environments. - Experience with continuous improvement across cost optimization, performance tuning and scalability of Data Engineering pipelines - Experience with enabling self service data engineering pipeline implementation capabilities for end users - Experience with using Co-pilot/AI capabilities to improve the productivity of Data Engineering pipeline