Head of Data, M&A Data Migration

The Head of Data, M&A Data Migration is an executive role responsible for leading the end-to-end data migration activities related to mergers, acquisitions, and divestitures. This role will be critical in ensuring the successful integration and/or separation of data across multiple business systems while maintaining data integrity, security, compliance, and business continuity.

This role will work cross-functionally with M&A Integration, IT, Business Units, and external partners to design and execute data migration strategies aligned with business objectives and timelines.This role will focus on the alignment of data definitions across the acquired processes and systems, with special focus on master, reference and metric data definitions.They will also lead data engineers and analysts, including third party personnel, in meeting objectives.

Additionally, this role will involve leading a team of data engineers to ensure the effective long-term collection, storage, and analysis of data to support business objectives. It will be instrumental in steering the company's data engineering and architecture agenda and advising on data technical vision and strategy. This role is crucial in developing and implementing data infrastructure, systems, and strategies that enable efficient data processing, data integration, and data governance. They are responsible for designing and maintaining scalable data architectures, data pipelines, and data warehouses to support the organization's data needs.

Our Head of Data, M&A Data Migration will also work closely with data scientists, analysts, and other stakeholders to understand their requirements and ensure that data is accessible, reliable, and properly structured for analysis and decision-making. They also monitor industry trends, new technologies, and emerging best practices in data engineering to drive innovation and continuous improvement within the department.

Strong leadership, strategic thinking, and technical expertise are essential for this role. They must have a deep understanding of data engineering concepts, programming languages, database technologies, cloud platforms, and data governance principles. They should also possess excellent communication and managerial skills to effectively lead and inspire their team and collaborate with cross-functional teams throughout the organization.

Key responsibilities

Strategy & Leadership:

• Define and own the enterprise data migration strategy for all M&A initiatives.

• Serve as the data migration authority during post-deal integration/separation (both pre- and post-close).

• Identify cost-effective data platforms, tools, and processes to support data migration including confidential clean-room procedures.

• Collaborate with senior leadership, integration teams, and technology leads to align on goals and priorities.

• Build, lead, and mentor a high-performing data migration team (internal and/or third-party) and in-time, assuming leadership for a high-performing data engineering team. This includes hiring and retaining top talent, setting performance expectations, conducting regular assessments, and fostering a collaborative and innovative work environment.

• Provide strategic direction and leadership for the data engineering department, aligning it with the organization's goals and objectives; this involves setting the vision, defining the roadmap, and adjusting priorities.

Planning & Execution:

• Develop and maintain detailed data migration plans, timelines, and dependencies. Establish contingency and rollback plans to ensure minimal disruption.

• Conduct pre-migration assessments, data audits, and mapping exercises including validating data dictionaries and mapping master data.

• Provide tools, accelerators, and best practices for data validation, reconciliation, and quality assurance processes.

• Assess and scope Day-1 consolidated reporting requirements and other reporting / analytic requirements for post-close.

• Ongoing, oversee the development, implementation, and maintenance of data pipelines to facilitate the collection, transformation, and loading of data from various sources into data warehouses or other data storage systems. This includes ensuring data quality, reliability, and timeliness.

Governance & Risk Management:

• Ensure all migrations comply with data privacy regulations (e.g., GDPR, CCPA).

• Implement robust data governance, access controls, and audit trails.

• Identify and mitigate risks related to data loss, corruption, or compliance breaches.

Stakeholder Management:

• Serve as the primary point of contact for data migration in M&A projects.

• Provide regular status updates and reporting to executive stakeholders.

• Partner closely with IT, Legal, HR, Finance, and Operations to understand business needs.

• Collaborate with cross-functional teams, such as data scientists, analysts, and business leaders, to understand their data needs and provide them with reliable and accessible data for analysis and decision-making; this includes facilitating promoting data literacy across the organization.

Other responsibilities:

• Stay up-to-date with emerging trends, tools, and technologies in the data engineering field. Evaluate new technologies and approaches to drive innovation, improve data engineering processes, and enhance overall data capabilities within the organization.

• Monitor and optimizing data engineering processes, systems, and infrastructure to ensure high performance, scalability, and cost-efficiency.

• Engage with external vendors, consultants, and partners to leverage their expertise, assess their solutions, and manage relationships to support data engineering initiatives.

Qualifications/Skills

The principal qualifications/skills of a Head of Data, M&A Data Migration include, but are not limited to:

• Minimum of twelve (12) years Enterprise Data Technology, Platform and Governance experience

• Minimum of ten (10) years Leading large data / technical team - Data Engineering, Data Analysts, Solution Architects, ML/AI Engineers, Data Scientists, Data Business Intelligence

• Minimum of five (5) years of M&A integration experience, preferably with multiple end-to-end data migration projects

• BS in Computer Science, Engineering or equivalent experience. Advanced degree (MS, MBA) desirable.

• Hands-on Experience visioning, building and running Enterprise Data & AI platforms at Scale (10+ Petabytes)

• Extensive knowledge and experience with cutting edge Data, Analytics, Data Science, ML and AI

• Familiarity with Azure suite of products - Azure Data Factory, Azure Blob Storage, Azure SQL Database, and PowerBI

• Advanced to Expert experience in Snowflake

• Strong working knowledge of industry leading Business Intelligence platforms (PowerBI, Tableau, Sigma, Looker, etc)

• Prior working knowledge of Salesforce, ServiceNow and Oracle Financials preferred

• Advanced working knowledge of all major cloud platforms Amazon Web Services (AWS), Microsoft Azure, or Google Cloud Platform (GCP). Understanding how to leverage cloud services for data storage, data processing, and scalability is essential along with enabling and operating in a multi-cloud environments

• Expert knowledge and implementation of Data Governance and Quality principles and frameworks

• Strong working knowledge of industry leading Data Governance and Inventory tools (e.g., Global IDs, Collibra)

• Programming Languages: Proficiency in programming languages such as Python, SQL, and Scala is essential for data engineering

• Data Warehousing: Understanding data warehousing concepts, including data modeling, dimensional modeling, and ETL (Extract, Transform, Load) processes, is crucial. Familiarity with data warehousing tools like Snowflake, dbt, Apache Spark.

• Database Systems: Proficiency in working with relational database systems like MySQL, PostgreSQL, or Oracle is necessary.

• Data Integration: Experience with data integration techniques and tools is important for combining data from multiple sources. Familiarity with tools like Apache Kafka, Apache Nifi, or cloud-based integration services helps in building robust and efficient data pipelines.

• Data Pipelines and Workflow Management: Experience in building and managing data pipelines using workflow management tools like Apache Airflow

• Data Security: Understanding data security principles and practices is crucial for protecting sensitive data and ensuring compliance with data privacy regulations. Knowledge of encryption techniques, access controls, and data masking methods are also valuable.

• Problem-solving and Analytical Thinking: Strong problem-solving skills and analytical thinking are essential for troubleshooting issues, optimizing data processes, and developing efficient solutions.