Bottomline is at the forefront of digital transformation. We are a growing global market leader uniquely equipped to address the changing needs of how businesses pay and get paid. Our culture of Working with and for each other enables us to delight our customers. We empower our teams to think like owners driving customer satisfaction, helping them grow their business and win in their markets.
We are looking for a Vice President of Engineering & Data Science to innovate, win, and grow with us.
This position can be based out of an open location within the US
Through close collaboration with the CTO and the EVP Product Strategy & Customer Delivery, other global centers of excellence, and the LOB leadership teams, the Vice President of Engineering & Data Science will develop and execute a data strategy for the product teams to help the organization evolve into a “data first” culture. Data is seen as both a differentiator within our existing products and services (customer delight) and, increasingly, the way we will innovate future products. Scope of responsibilities includes establishing a long-term vision, data and analytic maturity models, and a product roadmap. The VP of Engineering & Data Science will also oversee the execution of related initiatives. Additionally, the VP of Engineering & Data Science will help set a set of Key Performance Indicators, dashboards, OKRs, etc. to monitor and measure our progress in becoming data first and advancing our data and analytics maturity.
The role requires exceptional strategic leadership, deep influencer relationships, strong understanding of internal and external data use cases including data monetization. Success in this role will be further defined around the ability to work across business areas to define products and services centered on data and analytics which can be sold to existing customers and new markets. The VP of Engineering & Data Science will work closely with cloud operations, cloud architecture, security, R&D teams, innovation teams, and other centralized teams to align our technical strategy to enhance access to data through a common set of tooling and interfaces. While much of the company’s innovation agenda will be data first e.g., data monetization, customer analytics, there may be other areas of innovation where data plays an important “supporting role” e.g., business model innovation, API driven innovations, etc.
The VP of Engineering & Data Science works within Bottomline’s Data Strategy team. Reporting to the Chief Technology Officer, this role will engage closely with global centers of excellence and LOB product leadership teams. The Key Responsibilities include:
How You Will Impact:
- Data Engineering & Strategy: Ensure cohesiveness of our data strategy and communicate the same with Executive Leadership, LOBs, and other leaders. The VP of Engineering & Data Science will work closely with the Chief Technology Officer and others on
- A long-term data & analytics vision and a multi-year roadmap, developing a multi-year maturity model for data engineering, data analytics, and data science.
- Advice for organizational structure, processes and capabilities.
- Identifying a “crawl, walk, run” set of people, process, and platform-related initiatives to fulfill the vision over time. As a leader in the central teams, the VP of Engineering & Data Science will work with senior IT leaders and to create and support data-friendly architectures and applications that would better support product development i.e. analytics, data management, data quality, centralized data management i.e. he/she will be the ‘Product Owner’ responsible for developing, prioritizing and implementing a data roadmap using agile practices.
- Management & Execution: Lead the data engineering & data science teams with a dotted line on the corporate data strategy team through direct alignment and cooperation to enhance data first decision making within Bottomline, to embed systems of intelligence in our products, and to create new ways to monetize our data (and that of our clients) as new “+1 products”. Manage upward and ensure projects are delivered on time. The VP of Engineering & Data Science will oversee the execution of related data & analytics initiatives, e.g. projects around data enablement &/or master data management, data infrastructure, data quality, and best-in-class data and analytics capabilities. Set specific up-sales and cross-sales goals through data enablement initiatives in collaboration with each of product lines and set OKRs for the data engineering and data science teams to support those goals.
- Partnerships and Collaborations: The VP of Engineering & Data Science will get involved with data & analytics partnerships. Partnerships might be with data enablers e.g., consulting firms, cloud providers, academic organizations, industry groups, or API integration firms, or with tool providers e.g. BI tool vendors or cloud or API tool providers, or with startups doing innovative things with data.
- Data Governance: The VP of Engineering & Data Science will measure and optimize critical data assets and data initiatives relating to standard business standards, processes, and policies. For example, ongoing efforts will be “benchmarked” against a “maturity model” for data and analytics, and performance against such standards might be aligned with employee incentives. As part of governance, the VP of Engineering & Data Science will exhibit strong collaboration skills to work with varied stakeholders within the organization in refining their data requirements for various initiatives and data consumption requirements.
- Data Automation & Analytics Tools: Deploy data automation and analytical tools and services and build data and analytics partnerships with the business so that data becomes a key asset. For example, this role will drive Automation through effective metadata management leveraging innovative and modern tools, techniques, and architectures to partially or completely automate the most common, repeatable, and inefficient data integration tasks to minimize manual and error-prone processes and improve productivity.
What you will bring:
- Ability to build relationships and be a strategic partner vertically within the business unit, as well as with leadership and functional teams
- Ability to map joint organizational vision and long-term thinking, imagination, and idea generation
- Highly developed communication skills with the ability and comfort of working at executive levels
- Strong understanding of SaaS products and go-to-market best practices
- Exceptional ability to lead change using positive and collaborative methods, including agile methodologies and setting up initiatives with continuous development and collaboration processes
- Ability to map technical solutions to customer adoption and commercial success
- Able to produce results through strong program management, identifying and removing constraints
- Ability to focus on business value results, rather than solutions
- Must demonstrate acumen in business and function disciplines
- Experience with advanced analytics and data science tools and techniques, e.g AI/ML, NLP
- Understand and integrate innovation strategies and trends in technology and in big data and analytics to deliver value
- Be able to influence others, using personal rather than positional power
- Strong communication skills in writing, speaking and presenting
- Outstanding end customer/consumer relationship skills with an outside-in focus
- Strong humility, listening, creativity, and negotiation skills
- Experience defining and leading programs to define master data management including aggregating, consolidating, deduping, and standardizing across multiple data sources.
- Experience in working with large, heterogeneous datasets in building and optimizing data pipelines, pipeline architectures, and integrated datasets using traditional data integration technologies. These should include ETL/ELT, data replication/CDC, message-oriented data movement, API design, and access and upcoming data ingestion and integration technologies such as stream data integration, and data virtualization.
- Experience in working with SQL on relational databases from an open-source perspective and [Hortonworks Data Flow (HDF), Dremio, Informatica, Sisense, Denodo, Talend, others] from a commercial vendor perspective
- Experience with NoSQL and other types of databases and data processing technologies
- Experience working and processing unstructured and semi-structured data
- Experience in working with both open-source and commercial message queuing technologies [such as Kafka, JMS, Azure Service Bus, Amazon Simple queuing Service, others], stream data integration technologies such as [Apache Nifi, Apache Beam, Apache Kafka Streams, Amazon Kinesis, others] and stream analytics technologies such as [Apache Kafka KSQL Apache Spark Streaming Apache Samza, others].
Education and Experience:
- Extensive experience in increasingly responsible roles with at least 5 years of data-related executive leadership within a complex global business environment. 5+ years of experience working in data analytics, including creating new product offerings, implementing data governance, and understanding the underlying technologies needed to enable data innovation across an organization
- Strong demonstrated ability to skillfully hire, develop, lead, motivate, performance manage, and coach a cross-section of data and analytics professionals and managers
- Highly technical, confident at the operational management level, and comfortable in a software and/or engineering-heavy organization. Strong understanding of cloud business processes with hands-on experience in AWS and Microsoft Azure
- Experience with highly sensitive financial services and/or consumer data, and maintaining its security as a top priority; understanding big data and technology at scale
- Knowledge and expertise of US/EU privacy laws, including GDPR and CCPA
- Ability to thrive in a matrix environment where you can motivate teams to be aligned under a single objective in an efficient and effective way
- Ability to communicate at the team and executive levels, risks, and change management in a clear and concise manner
- Strong understanding of data warehouse design principles
- Proven Data Governance track record with an emphasis on data policies, standards, and processes design, administration, and compliance monitoring
- Strong experience with Data Integration, Data Warehouse, and Business Intelligence program development and implementation
- Strong knowledge in data management best practices and current leading developments in the data analytics field
You’ll love Botttomline because in everything we do we seek to delight our customers and we are passionate about building a company of which we can all be proud, and this starts with building amazing teams filled with team members that challenge you every day.
Start your #LifeAtBottomline