Analytics Product Manager

Job Description Summary

Responsible for expanding and optimizing overall Digital Technology data products, analytics and data engineering solutions. Will be supporting data analysts, solution architects, data scientists and BU engagement partners to deliver data analytical and reporting solutions. Accountable for managing multiple data domains and delivering wing to wing data solutions that include establishing data pipelines, data flows, standardizing data models and setting up complex data transformation rules. Will be responsible for managing the data products, reports and domain outcomes released to production through the life of the analytics solution.

Leverages deep technology, process and domain expertise to drive execution across multiple interdependent projects within a program; ensures outcomes delivery on time, within scope and budget and of desired quality. Defines execution standards and processes to be followed by all data architects within Vernova Data Lake analytics solution landscape.

Job Description

Roles and Responsibilities In this role, you will:

• Be responsible for coordinating multiple interdependent projects forming a program in support of the Digital Technology and Data & Analytics function

• Work on large-size project or a program comprising of multiple projects impacting a single or multiple domains and be able to coordinate across different analytics product managers to identify direct and derived outcomes

• Work with business functions to gather requirements to document functional specifications and process flows, drive design and development strategy and funding requirements

• Handle multiple projects across partner and business process leaders and provide clear visibility on program plan, progress and execution

• Consult analytics leader on analytics "best practices" process implementation and drive end-to-end process implementation or improvements initiatives

• Promote user adoption by facilitating user acceptance testing and training, data literacy and change management

• Identify opportunities for improvements and enhancements to maximize the use of data lake analytics solutions throughout the organization

• Partner with the data security and audit teams to ensure compliance and hygiene from GITC, SOx, and controls

• Works on AI adoption leveraging Databricks services to enable self-service and LLM services through data catalog and AI space metadata enrichment

• Develop and maintain an integrated data architecture spanning the relational and nonrelational data landscape

• Drive reusability and ensure extensibility of overall data architecture

• Lead data profiling and data analysis for source systems, manually maintained data, and target data repositories

• Design and validate both logical and physical data models for standard model and composable architecture in the Vernova Data Lake

• Standardize automated Extract, Transform & Load (ETL) jobs based on data mapping specifications

• Define metadata structures needed for building reusable ETL components

• Design common data structures for handling reference datasets and master data.

• Lead the efforts to build data quality rules

Education Qualification

• Bachelor's Degree in business, computer science or in "STEM" Majors (Science, Technology, Engineering and Math)

• Minimum of 3 years of experience working with Data Management, Business Intelligence, or Data Analytics

• A minimum of 6 years of technical experience along with established leadership credentials across disciplines and functions within a product

Desired Characteristics

Technical Expertise:

• Working knowledge of mathematics, statistics, text mining, and modeling skills

• Willingness to learn and develop critical data science skills (e.g. statistical analyses, advanced data visualization, data mining, and data cleansing/transformation)

• Working knowledge of Big Data programming environments and Competency in major analytics software packages and programming environments (e.g. Databricks; Python, R, Java etc. in big data paradigms)

• Hands on experience working with database technologies, including Oracle, SAP, SQL / NoSQL platforms, visualization applications such as Spotfire or Tableau

• Familiarity with writing SQL queries and working with databases

• Exposure to unstructured datasets and ability to handle XML, JSON file formats

• Conducting exploratory data analysis and generate visual summaries of data. Identify data quality issues proactively