Business Analytics Engineer Enterprise - Oahu

Hawaiian Electric•Honolulu, HI

2d•Onsite

About The Position

The Business Analytics Engineer is a recognized subject matter expert in data analytics, supporting enterprise-wide or domain-specific work by leading business analytics services and data-driven solutions across data science disciplines. The position optimizes data models and business aggregate (“last mile”) data pipelines, treating data as a strategic enterprise asset to enhance business performance, improve process effectiveness, predict outcomes, and support data-driven decisions. The role explains and advocates for effective data use, advising and teaching others while turning data into information, insight, and business action. It works across all functional data domains and enterprise-wide at Hawaiian Electric Company, Hawaii Electric Light Company, and Maui Electric Company. The role extracts insight from high-volume, high-dimensional data using statistical analysis, algorithms, predictive modeling, experimentation, and pattern recognition. It converts data models into scalable analytical solutions, creates data modeling and mining processes, and applies software engineering practices to produce clean, transformed data. It supports machine-learning development and research into artificial intelligence applications, and supports or leads delivery and ongoing improvement of analytical solutions that provide business value and meet key performance indicators. The Business Analytics Engineer consults on enterprise or domain strategy, identifies opportunities to use data, and provides leadership and direction to analytics teams and analysts. It researches data acquisition and new uses for existing data, works with subject matter experts and data science teams to identify appropriate data and data management systems, and mentors employees and data consumers in analytical tools, techniques, technologies, information, and business intelligence. It monitors industry, functional, and technology trends and maintains multi-year analytics plans, roadmaps, and strategies for leadership. The role must remain available for scheduled and unscheduled work, which may occasionally occur outside normal business hours and on weekends, and may participate in assigned Company emergency response activities.

Requirements

Bachelor’s degree in data analytics, data science, computer science, management information systems, business analysis, business management, engineering, finance, statistics, or a related field, or equivalent work experience.
Thorough knowledge of business and data analysis, including work with unstructured data and business data requirements elicitation, documentation, standardization, and validation.
Thorough to extensive knowledge of data analytics operations and delivery concepts, principles, standards, tools, techniques, and methodologies for solving complex problems.
Familiarity with Agile methodology and DevOps, and experience leading cross-functional teams in a dynamic environment.
Proficiency with analytical tools such as PySpark, R, Python/Jupyter, SAS, Power BI, Tableau, or MATLAB.
Strong knowledge of database programming languages such as SQL and PL/SQL for relational databases, as well as emerging nonrelational databases and distributed data or computing tools.
Thorough experience with data discovery, analytics, and business intelligence tools such as Power BI and Tableau, including semantic-layer-based data discovery.
Working knowledge of statistical methods and data-mining techniques such as predictive modeling, segmentation, and regression; experience working with data science teams to refine and optimize data science and machine-learning models and algorithms.
Demonstrated leadership, interpersonal, written, oral, and listening skills to work effectively with all company levels and external contractors and consultants.
Strong analytical skills and data literacy, including the ability to describe business use cases and outcomes, data sources and management concepts, analytical options, and solutions to business problems.
Ability to communicate complex information to technical and non-technical audiences, facilitate multi-level briefings, and deliver strong presentations using storytelling and other techniques to guide and inspire.
Ability to conceptualize new ideas quickly and learn new technologies on the job.
Ability to communicate difficult concepts, negotiate adoption of new perspectives across the enterprise, mentor teams, and facilitate knowledge transfer.
At least 7 years of experience in data management disciplines, including data integration, modeling, optimization, and data quality, and/or other areas directly relevant to data engineering and analytics.

Nice To Haves

Azure experience; understanding of distributed computing and cloud technologies.
PySpark experience.
Working knowledge of utility business, business, and/or engineering principles and practices.
Experience with commercial or open-source Big Data discovery and analysis platforms, particularly Spark and Databricks ML; practical Databricks experience in machine learning or data science is strongly preferred.
Three to five years of project management experience working in cross-functional teams with business stakeholders on multi-departmental data management and analytics initiatives.
Electric utility experience and experience with Azure, Databricks, SAP, Big Data, or cloud-based architecture.
Data analytics, product, or industry certification.

Responsibilities

Extract knowledge and insights from high-volume, high-dimensional data using statistical analysis, algorithms, predictive modeling, experimentation, and pattern recognition.
Convert new or existing data models into scalable analytical solutions; create data modeling, mining, and production processes; and apply software engineering best practices to analytics code.
Support development of machine-learning models and research into new artificial intelligence applications.
Support or lead timely delivery, management, enhancement, and upgrades of analytical solutions that improve business value, data reliability, efficiency, quality, and usability, and meet KPIs.
Consult on enterprise or domain strategy, understand business priorities, identify data opportunities, and provide leadership and direction to analytics teams and analysts.
Research data acquisition opportunities and new uses for existing data; identify how data applies to business operations; and work with data subject matter experts and data science teams to determine data needs and appropriate data management systems.
Mentor and coach employees, data analytics community members, business users, and other data consumers in adopting analytical tools, techniques, technologies, information, and business intelligence tools.
Monitor industry, functional, and technology trends; guide business partners on emerging opportunities and product or technology development; and maintain multi-year analytics plans, roadmaps, and strategy for leadership.
Remain available for scheduled and unscheduled work, including occasional work outside normal business hours and on weekends.
Participate in Company emergency response activities as assigned, including preparation activities.