About Oracle Utilities  

At Oracle Utilities, we’re working on one of humanity’s greatest challenges: Climate Change. Our utility customers across the globe give us energy usage data for tens of millions of their customers, which we then analyze and aggregate using state-of-the-art technologies. We share this analysis with utility customers to make them aware of their energy consumption and foster better energy management behavior.  

  

Our efforts so far have saved 40 TWh of energy which is enough to power 6.3 million passenger vehicles and would have otherwise required us to burn an additional 29.9 billion pounds of coal.  

Join us to help make the world a better place.  

 

About the Team 

The Opower Data Engineering group at Oracle Industry Analytics & Innovation organization is a cross-functional team of application developers, big data engineers, data platform ops engineers, and data scientists who work collaboratively to build robust ML insights pipelines, design scalable ETLs, and deliver advanced analytics solutions.  This team brings forth ML/AI/GenAI powered insights to millions of households across the world. This team builds on top of modern Oracle Cloud technologies to create transformative solutions that drive the future of our business.  As a Senior Data Engineer & Data Analyst, you’ll play a critical role in building data and ML solutions, driving business decisions and making petabytes of data accessible, reliable, and impactful. 

  

About the Role 

As a Senior Data Engineer & Data Analyst, you will harness the power of data to solve complex problems and influence strategic initiatives. You’ll work closely with stakeholders across various domains, ensuring that data-driven decisions are at the core of our operations. If you are passionate about data analysis, visualization, feature engineering, data quality and integrity - this role is perfect for you. 

 

Key Responsibilities 

Design and Build ETL Workflows: Create scalable ETL processes using Spark and Python to efficiently process and transform large datasets.  Develop and Evolve Data Models: Design and enhance data models, pipelines, and schemas to support advanced analytics and reporting applications and insight generation pipelines  Integrate Data with Visualization Tools: Implement and maintain dataset integrations with data visualization platforms, ensuring accurate mapping of data elements to business dimensions and facts.  Conduct Exploratory Data Analysis: Analyze data to validate data accuracy, detect anomalies, address questions/concerns from stakeholders  Feature Engineering for ML Models: Collaborate with data scientists to develop and optimize features for machine learning applications, supporting seamless ML Ops workflows.  Optimize Data Infrastructure: Work closely with cross-functional teams to enhance data quality, reliability, and scalability across the organization.  Leverage Modern ML and AI Services: Explore and integrate cutting-edge Oracle cloud-based AI services, including Generative AI, speech recognition, and other advanced technologies, to enhance data engineering workflows and capabilities. 

 

Required Skills and Qualifications 

5+ years of experience in data analysis, data engineer, ML engineer or a similar role. 

Technical Expertise: 

Proficiency in Python coding for data manipulation, ETL, and pipeline development.  Proficiency in SQL and experience working with modern data warehouses (e.g., Oracle Autonomous Data Warehouse, Snowflake, BigQuery, Redshift).  Solid understanding of data modeling principles and experience in designing dimensional models (facts and dimensions). 

Data Visualization & Analytics: 

Experience integrating datasets into visualization tools (e.g., Tableau, Power BI, Oracle Analytics Cloud), mapping data elements to business dimensions and facts.  Proven ability to conduct exploratory data analysis (EDA) and deliver actionable insights through dashboards and reports.  Experience with data modeling for analytics use cases. 

ML/Data Ops Knowledge: 

Hands-on experience in feature engineering for machine learning models  Familiarity with ML Ops concepts   Collaboration and Communication:  Strong problem-solving and critical thinking skills, with the ability to translate technical insights into clear explanation for non-technical stakeholders.  Ability to work cross-functionally in a dynamic environment, collaborating with data scientists, engineers, and business teams. 

 

Preferred Experience: 

Experience with Java or JVM language is a big plus Experience with Apache Spark for large-scale data processing. Knowledge of orchestration tools like Apache Airflow or dbt.  Exposure to cloud platforms such as Oracle Cloud, AWS, GCP, or Azure.  Familiarity with version control systems like Git. 

Career Level - IC3