VP, Data Engineering & Architecture

Company Description
Zayo provides mission-critical bandwidth to the world's most impactful companies, fueling the innovations that are transforming our society. Zayo's 141,000-mile network in North America and Europe includes extensive metro connectivity to thousands of buildings and data centers. Zayo's communications infrastructure solutions include dark fiber, private data networks, wavelengths, Ethernet, and dedicated Internet access. Zayo serves wireless and wireline carriers, media, tech, content, finance, healthcare and other large enterprises.
Vice President - Data Engineering & Architecture
The Vice President of Data Engineering is a senior executive responsible for overseeing and managing the data engineering department within an organization. This role involves leading a team of data engineers and collaborating with other departments to ensure the effective collection, storage, and analysis of data to support business objectives. Instrumental in steering the company's data engineering & architecture agenda and advising on all data technical vision, strategy and 3-5 year roadmap & data capabilities from the analyst level to C-Suite across Zayo.
The Vice President of Data Engineering plays a crucial role 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.
In addition, the Vice President of Data Engineering works 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. The Vice President of Data Engineering 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
The principal responsibilities of a Vice President of Data Engineering include, but are not limited to:

• The data, technical and subject matter expert for Zayo's Global Data platform with end to end accountability of Zayo's data platform
• Has profound grasp of data engineering concepts, programming languages, database technologies, cloud platforms, and data governance principles AT SCALE.
• Visioning 3-5 year data / technical strategy & roadmap while mentoring resources and driving team to execute tactically supporting Zayo's data target state architecture
• Responsible for leading cross Zayo collaboration with cross-functional teams, such as data scientists, analysts, and business leaders, to understand their complex data needs and provide them with reliable and accessible data for analysis and decision-making.
• Establishes strategy and comprehensive leadership direction in the areas of data architectures, data pipelines and data warehouses to support the organization's data needs
• Influence and drive technical details of the data platform while partner with business owners to enable / drive business value to Zayo by treating data as an asset
• Responsible for leading a team and collaborating with other departments to ensure the effective collection, storage, and analysis of data to support business objectives
• Reports directly to the Chief Data Officer and will have regular interactions with other C-level executives as well as the Board of Directors in all aspects of Zayo's Data Platform and Frameworks
• Technical and data subject matter expert of Zayo's Data Platform while being the de facto successor to Zayo's Chief Data Officer (CDO).

Key Areas of Responsibilities:
Leadership and Strategy: Providing 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 establishing the long-term data engineering strategy.
Team Management: Building and managing 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.
Data Architecture and Infrastructure: Designing and implementing scalable data architectures and infrastructure that support the organization's data processing and storage needs. This involves selecting appropriate technologies, tools, and platforms to ensure efficient data flow, data integration, and data governance.
Data Pipeline Development: Overseeing 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.
Data Governance and Compliance: Establishing and enforcing data governance practices and standards to ensure data quality, privacy, and compliance with relevant regulations. This involves defining data policies, access controls, and data security measures.
Collaboration and Stakeholder Management: Collaborating 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 data-driven insights and promoting data literacy across the organization.
Technology Evaluation and Innovation: Staying up-to-date with emerging trends, tools, and technologies in the data engineering field. Evaluating new technologies and approaches to drive innovation, improve data engineering processes, and enhance overall data capabilities within the organization.
Performance Monitoring and Optimization: Monitoring and optimizing data engineering processes, systems, and infrastructure to ensure high performance, scalability, and cost-efficiency. This involves identifying bottlenecks, implementing performance enhancements, and conducting regular audits and assessments.
Vendor and Stakeholder Management: Engaging with external vendors, consultants, and partners to leverage their expertise, assess their solutions, and manage relationships to support data engineering initiatives.
Qualifications / Skills

• 15+ years Data Technology, Platform and Governance experience
• 10+ years Leading large data / technical team - Data Engineering, Data Analysts, Solution Architects, ML/AI Engineers, Data Scientists, Data Business Intelligence
• Hands-on Experience visioning, building and running Enterprise Data & AI platforms at Scale (10+ Petabytes)
• Extensive market knowledge and experience with cutting edge Data, Analytics, Data Science, ML and AI
• Expert knowledge of Azure suite of products - Azure Data Factory, Azure Blob Storage, Azure SQL Database, Azure Synapse Analytics, Azure Cosmos DB, Azure Stream Analytics, Azure HD Insight and knowledge of Azure Databricks and PowerBI
• Advanced to Expert experience in Snowflake
• Advanced to Expert experience with Tableau and strong working knowledge of other industry leading Business Intelligence platforms (PowerBI, Microstrategy, Qlik, 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
• Extensive experience with on-prem to cloud migration at scale (> 10 petabytes)
• Expert knowledge and implementation of Data Governance and Quality principles and frameworks.
• Knowledge of data quality assessment techniques, data profiling, and data cleansing methods is essential for ensuring data accuracy and reliability. Understanding data governance principles and best practices is also important for maintaining data integrity and compliance.
• Advanced to Expert knowledge of Data Product and Inventory principles and tools. Specifically, Data Libraries and Business Ready Data Sets (BRDs) establishment and management, Critical Data Elements, Technical Metadata, Business Metadata.
• 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. Python is often used for data manipulation and scripting, SQL for database querying and manipulation, and Scala for working with big data frameworks like Apache Spark.
• 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, Apache Hadoop, Apache Hive, and Apache Spark.
• Database Systems: Proficiency in working with relational database systems like MySQL, PostgreSQL, or Oracle is necessary. Knowledge of NoSQL databases like MongoDB or Apache Cassandra is also valuable
• 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 Processing Frameworks: Proficiency in working with big data processing frameworks like Apache Spark or Apache Flink is highly valuable to enable distributed data processing and analysis at scale.
• Data Pipelines and Workflow Management: Experience in building and managing data pipelines using workflow management tools like Apache Airflow, Luigi, or AWS Step Functions.
• 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.

Base pay range: $225,000 - $275,000, commensurate with experience
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Benefits, Rewards & Wellness

• Excellent Health, Dental & Vision Insurance
• Retirement 401(k) Savings Plan
• Fitness membership discounts
• Generous paid time off policy including paid parental leave

Zayo provides equal employment opportunities to all employees and applicants for employment and prohibits discrimination and harassment of any type without regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state, provincial or local laws.
This policy applies to all terms and conditions of employment, including recruiting, hiring, placement, promotion, termination, layoff, recall, transfer, leaves of absence, compensation and training.