Senior Director – Enterprise Data and AI Architecture and Engineering Practice

Job Description

Job Description:

Locations: Chicago, New Jersey, London (UK)

Job Description:

At Mars Snacking, we are on an ambitious journey to rewire and almost double the size of our business by 2030. We see digital and data as an increasing multiplier of value across not just Mars Wrigley, but also the Health and Wellness and Retail global business units of Mars Snacking.

With this in mind, we recently established the Chief Data Office (CDO) Organization to define and execute the enterprise data, analytics, and AI strategy for the company. In collaboration with our various Global Process Experience Owners (GXOs), Regional Champions and CIOs, Functional Business and Digital Technology Leaders, and Global Business Services teams, we will establish robust data quality practices, effective operating models, evolve data and AI literacy, and implement durable technology capabilities to govern our data as an asset from end to end. The CDO organization will serve as an accelerator to our Mars Snacking’s next generation of data-led AI journey.

The global director of enterprise data & AI architecture and engineering is part of the leadership team of the Chief Data Office Organization within Mars Snacking. In this pivotal role, the global director will be responsible for D&A technology ecosystem transformation and for continuously assessing and improving our enterprise data and AI architecture and engineering practices to remain composable and modular, so data is easily findable, accessible, interoperable, secure, and reusable to all Mars Snacking associates.

The role requires a combination of strategic thought leadership, strong technical competency to bring to life best-in-class industry leading data analytics and AI architecture and engineering practices, and a commitment to drive continuous improvement in building reusable and scalable data products for a significant data-led AI advantage.

What are we looking for?

Minimum Qualifications:

• Bachelor’s degree in computer science or business analytics or related major with equivalent business experience. MBA/Master's degree preferred.
• Proven 15+ years of experience in leading enterprise scale data and analytics architecture and engineering transformation from the ground-up.

Preferred Qualifications:

• Hands-on data architecture experience in conceptual, logical, physical, and semantic data modelling.
• Strong technical experience of building a sustainable data engineering foundation for advanced analytics, GenAI and LLMs to deliver the expected efficiency and data democratization.
• Strong knowledge of latest advancement in effectively building and deploying data mesh, knowledge graphs, and data fabric.
• Experience with multiple cloud-based data platforms (e.g., Microsoft Azure, Google Cloud, SAP Datasphere or SAP Datawarehouse cloud) and an expert ability to use various tools and frameworks that support data engineering tasks, such as Databricks, Snowflake, Spark, Kafka, Airflow, Azure, GCP, etc.
• Strong knowledge and ability to explore and analyze data using various methods and tools, such as SQL, Python, R, Spark, Pandas, etc.
• Experience developing enterprise data integration strategy for simplification where possible. This includes assessing and implementing fit-for-purpose data integration tools such as Informatica, Mulesoft, SAP etc. in collaboration with our shared service and cross-segment digital technology teams.
• Excellent working knowledge of connected data foundation enablers such as metadata management, data cataloguing, data integration/API management, data quality, master, and reference data management
• Expert in data visualization with the ability to present and communicate data using various techniques and tools, such as charts, graphs, dashboards, reports, etc.
• Expertise leading agile product management and working with scrum teams.
• Experience with SDLC methodologies.
• Experience with data modelling toolsets like ERStudio and architectural frameworks such as TOGAF or Zachmann
• Data transformation experience within the Consumer Products & Goods industry is preferred but not required.
• Experience engaging with technical and non-technical audiences to understand their priorities and related data skill needs.
• Experience working with all levels of employees from entry-level to senior leadership.
• Experience working at Big 4 consulting firms is a plus.

What will be your key responsibilities?

Key Leadership Responsibilities:

Strategic Leadership: Collaborate with the digital, data and analytics technology leadership within Mars Snacking as well as across Mars Inc. and own the charter to lead and drive the 2-3 year accelerated roadmap to transform and build the next generation of Mars Snacking data, analytics, and AI architecture and engineering ecosystem aligned to the broader Enterprise Data Strategy, Digital Core, and D30 business objectives and value targets.

Capability leadership: Own and guide the team on enabling business with multiple enterprise Data, Analytics, and AI (DAAI) capabilities such as 1) Enterprise Data Engineering 2) Enterprise Data Domain and Business Information Architecture 3) Data Science, AI/ML Ops and GenAI foundation 4) Data Integration/API Strategy and Management

People Leadership: Lead, recruit, and retain a team of data, analytics and AI technology practitioners reporting directly as well as dotted line from other groups. This includes mentoring, coaching, and providing guidance for building technical competency among leaders, peers, and subordinates within Mars Snacking as well as cross-segment across Mars Inc.

DAAI Architecture and Engineering Practice Development Responsibilities:

Enterprise Data Mesh Architecture:

• Lead the transition to a new enterprise data mesh architecture, emphasizing modularity, scalability, security, and interoperability of cross-functional data domains.
• Collaborate with cross-functional global teams to design and implement data products that adhere to and promote adoption of data governance standards.
• Ensure seamless integration of data across the organization.

Data Ingestion Strategy and Governance:

• Modernize data engineering practice for a durable enterprise data foundation.
• Develop and execute disciplined, scalable, and sustainable data ingestion strategies.
• Establish guidelines and standard operating procedures for data acquisition, transformation, and loading (ETL).
• Enable data lineage and monitor data quality and compliance.

Enterprise Data Fabric and Knowledge Graph:

• Create a robust enterprise connected data fabric that enables self-service AI/ML operations, automation of data management, and data science experiments.
• Implement a knowledge graph to enhance data discoverability and relationships.
• Build, maintain, and mature the foundational metadata management.
• Foster a culture of data literacy and exploration.

Data Integration and API Management:

• Develop future-proof data integration strategy
• Lead data integration efforts, connecting disparate systems and sources.
• Design and manage APIs for seamless data exchange.
• Optimize data flows and reduce latency.

Generative AI (GenAI) architecture and engineering foundation

• Develop robust GenAI foundation architecture considering scalability, efficiency, and security.
• Develop, enhance, and continuously optimize GenAI algorithms for tasks such as data content generation, recommendation systems, and natural language processing (NLP).
• Collaborate with data engineering team to build data pipelines for GenAI model training, prompt engineering, and ensuring data quality and reliability.
• Ensure adherence to data and AI policy and governance framework and support ethical and responsible AI enterprise forums.

Technical Data Dictionary:

• Build and maintain a comprehensive technical data dictionary.
• Define data entities, attributes, relationships, and business context.
• Collaborate with stakeholders to ensure accurate documentation.

Knowledge asset management and technical white paper publications

• Educate existing talent and build pipeline for next generation of data and AI architecture and engineering talent with a robust knowledge asset management.
• Develop and publish technical white papers internally and externally focussed on cognitive analytics and innovative data and AI architecture and engineering practices.

In addition to the strategic and tactical responsibilities, the ideal candidate also exhibits following traits:

• Self-starter with strong business acumen who understands organizational issues, and challenges.
• Communicate technical solutions to business partners in a meaningful way.
• Entrepreneurial and collaborative in nature who enjoys being able to navigate changing direction of programs and strategies as they evolve.
• Skills to understand the culture and emotional blockers for people’s willingness to embrace change.
• Executive presence with experience presenting data related ideas and program updates flawlessly to executives (VP and above), senior level directors, and digital technology and business leaders.

What can you expect from Mars?

• Work with over 130,000 diverse and talented Associates, all guided by the Five Principles.
• Join a purpose driven company, where we’re striving to build the world we want tomorrow, today.
• Best-in-class learning and development support from day one, including access to our in-house Mars University.
• An industry competitive salary and benefits package, including company bonus.

Mars is an equal opportunity employer and all qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability status, protected veteran status, or any other characteristic protected by law. If you need assistance or an accommodation during the application process because of a disability, it is available upon request. The company is pleased to provide such assistance, and no applicant will be penalized as a result of such a request.