Finance Data Product Engineer

Analog Devices•Wilmington, MA

About The Position

About Analog Devices Analog Devices, Inc. (NASDAQ: ADI ) is a global semiconductor leader that bridges the physical and digital worlds to enable breakthroughs at the Intelligent Edge. ADI combines analog, digital, and software technologies into solutions that help drive advancements in digitized factories, mobility, and digital healthcare, combat climate change, and reliably connect humans and the world. With revenue of more than $9 billion in FY24 and approximately 24,000 people globally, ADI ensures today's innovators stay Ahead of What's Possible™. Learn more at www.analog.com and on LinkedIn and Twitter (X) . Finance Data Product Engineer Location: Wilmington, MA Responsibilities As a Senior Data Engineer, you be responsible for the design and development of data pipelines, automation, and visualization tools to enable modern AI and analytics solutions. Guide the expansion of our data products and solutions by advising and influencing business leaders and senior stakeholders with data-driven insights. Partner with Product owners, data platform and infrastructure teams to architect and connect high quality, resilient data feeds from across the enterprise. Lead the execution of large scale, complex data and analytics efforts, as well as mentor complex projects using a wide breadth of data science and analytical techniques. Develop in-depth subject matter-expertise in understanding key strategic data sources and data lineage within the organization. Scope key business challenges, identify the right data and provide direction to data analysts, data analytics product managers, data scientists, data engineers and business stakeholders. Communicate the value of data clearly by articulating both the high-level concept and detailed user stories for feature enhancements and convince teams to adopt data-driven improvements. Drive innovation by creating new frameworks, standards, prototypes, and automation projects in relevant areas.

Requirements

8+ years of experience in software engineering with deep expertise in data infrastructure, distributed systems, or backend platform engineering.
Strong experience with tools like Snowflake, dbt, Kafka, Airflow, Spark, and cloud-native technologies (e.g., AWS, GCP, or Azure).
Deep knowledge of Python, SQL, database design, and master data strategies.
Experience defining, architecting, and rolling out data products, including ownership of data products through their entire lifecycle.
Experience working with analytics reporting for Finance, Revenue , FP&A business areas.
Experience with version control systems (e.g., Git) and CI/CD workflows for data engineering projects.
Bachelor’s degree or equivalent experience in computer science, Information Science, Information Technology or Engineering or a related Field.
Experience mentoring and leading junior technical staff, incorporating modern software development tools and practices.
A confident peer influencer with strong communication skills who quickly establishes credibility and is capable of leading cross-functional technical teams.

Nice To Haves

Experience with BI technologies like PowerBI and Tableau is a plus.

Responsibilities

Design and development of data pipelines, automation, and visualization tools to enable modern AI and analytics solutions.
Guide the expansion of our data products and solutions by advising and influencing business leaders and senior stakeholders with data-driven insights.
Partner with Product owners, data platform and infrastructure teams to architect and connect high quality, resilient data feeds from across the enterprise.
Lead the execution of large scale, complex data and analytics efforts, as well as mentor complex projects using a wide breadth of data science and analytical techniques.
Develop in-depth subject matter-expertise in understanding key strategic data sources and data lineage within the organization.
Scope key business challenges, identify the right data and provide direction to data analysts, data analytics product managers, data scientists, data engineers and business stakeholders.
Communicate the value of data clearly by articulating both the high-level concept and detailed user stories for feature enhancements and convince teams to adopt data-driven improvements.
Drive innovation by creating new frameworks, standards, prototypes, and automation projects in relevant areas.