Data Engineer II

Welcome to the intersection of energy and home services. At NRG, we’re driven by our passion to create a smarter, cleaner and more connected future.

Vivint Smart Home, an NRG owned company, is a leading smart home company in the United States, dedicated to redefining the home experience with intelligent products and services. We find purpose in proactively protecting and keeping our customers connected to home, no matter where they are. Join the Smart Home team to create smarter, safer and more sustainable homes.

JOB DESCRIPTION

 

Vivint pioneers cutting-edge smart home security solutions, empowering customers to live a safer, smarter, and more sustainable life. We seek passionate, innovative engineers eager to transform the daily lives of millions through groundbreaking solutions. We blend intelligent hardware, real-time sensor data, and AI to transform how people interact with their homes.

 

About This Role

The Product Analytics team at Vivint partners with Product, Operations, Finance, and Engineering to transform data from devices, services, and customer interactions into meaningful insights that improve decision making across the business. We are seeking a Data Engineer to help build and maintain the data pipelines and analytics infrastructure that power product analytics, operational intelligence, financial reporting, and mobile app insights. This role focuses on developing reliable data pipelines, enabling analytics workflows, and helping teams access trusted data to drive product innovation and operational improvements.

 

• Data Pipeline Development - Build and maintain scalable ETL/ELT pipelines that ingest and transform data from application services, device telemetry, mobile apps, and business systems.
• Analytics Data Modeling - Develop clean, well-documented data models and analytics-ready datasets that support dashboards, experimentation, and reporting across product, operational, and finance domains.
• Mobile & Product Analytics Instrumentation - Work with product and engineering teams to ensure new features and mobile applications are properly instrumented for event tracking and analytics.
• Operational & Business Metrics Enablement - Build data pipelines and data marts that support operational reporting, customer lifecycle metrics, and financial performance analysis.
• Data Quality & Observability - Implement monitoring, testing, and validation checks to ensure reliable, trustworthy datasets for analytics and decision making.
• Collaboration with Analysts & Data Scientists - Partner with analytics teams to translate business requirements into well-structured data pipelines and datasets that support experimentation and insights.
• Data Platform Contribution - Support the maintenance and optimization of cloud data infrastructure including data warehouses, orchestration systems, and streaming pipelines.

Required Qualifications

• Bachelor’s in Computer Science, Engineering, Data Science, or related quantitative field, or equivalent experience.
• Experience: 2 - 3 years of experience building or maintaining data pipelines, analytics infrastructure, or backend systems.
• Programming: Proficiency in Python and SQL for data processing and transformation.
• Data Platforms: Experience with modern data warehouses such as BigQuery, Snowflake, or Redshift.
• Data Processing: Familiarity with distributed data processing frameworks such as Spark or Databricks.
• Cloud Platforms: Experience working with cloud platforms such as GCP, AWS, or Azure.
• Data Modeling: Understanding of data modeling principles and schema design for analytics workloads.
• Collaboration Skills: Ability to work cross-functionally with analysts, product managers, and engineers to translate requirements into reliable data systems.

Preferred Qualifications

• Experience supporting product analytics or mobile analytics platforms (e.g., Mixpanel, Amplitude, Firebase).
• Familiarity with event-based analytics pipelines and user behavior tracking.
• Experience with data orchestration tools such as Airflow, Dagster, or Prefect.
• Exposure to real-time or streaming data systems (Pub/Sub, Kafka).
• Experience working with NoSQL databases such as MongoDB or Firestore.
• Familiarity with analytics visualization tools such as Tableau or Looker.
• Experience implementing data