Senior Data Engineer

About Us

Charger Help! Inc., a California-based tech company, offers impactful solutions that power-up the EV ecosystem by providing Reliability as a Service (RaaS), data-driven insights via our EMPWR platform, and Learning & Development to prepare the next generation of EVSE technicians. We pride ourselves on being a trusted partner within our industry.

In the EVSE landscape, we're not just solving a challenge; we're driving the charge towards a sustainable future. Our mission is crystal clear: maximizing EV charging station uptime to unlock the full potential of mass EV adoption. Through innovation, integrity, and an unwavering commitment to quality and safety, we foster a culture of excellence and trust. If you join us you’ll be part of a dynamic team that’s leading EV reliability.

Together, we're not just changing the industry; we’re helping shape the future of transportation and environmental sustainability.

We are seeking a highly skilled and experienced Senior Data Engineer to spearhead Charger Help’s goal of using data as a strategic asset in improving EV charger reliability. The Senior Data Engineer will be responsible for architecting, implementing, and maintaining scalable and robust data infrastructure to power Charger Help’s reliability management software tools. This role requires strong technical acumen, excellent problem-solving and communication skills, and a passion for driving innovation in the EV charging industry.

The ideal candidate will have a proven track record in designing, building, and maintaining high-quality, scalable data solutions, including pipelines and analytics services for both structured and unstructured data from diverse sources. They will be skilled in developing user-facing solutions for asset reliability management and collaborating with teams and customers to create effective metric datasets and dashboards. Additionally, the candidate will have experience building data quality checks and monitoring systems to ensure data integrity and reliability across platforms.

Design, Build, and Maintain Scalable Data Solutions

• Objective: Develop high-quality, scalable data solutions that support both structured and unstructured data across multiple sources.

• Responsibilities:

  • Design and implement data pipelines that process and integrate data from diverse sources (both internal and external).

  • Build analytics services to handle various data types and generate insights for internal teams and customers.

  • Develop and maintain data storage and management solutions that ensure data accessibility, security, and scalability.

Optimize Data Flow and Collection Across Multiple Sources

• Objective: Improve the efficiency of data collection and movement, ensuring smooth integration across various data sources.

• Responsibilities:

  • Implement best practices for data flow optimization, including batch processing, streaming, and real-time data ingestion.

  • Collaborate with teams to ensure seamless integration between internal systems and external data providers.

  • Conduct regular data audits to identify and resolve bottlenecks or inefficiencies in the data flow.

Design Core Data Models for Asset Reliability Management

• Objective: Create data models and structures that support the reliability management of electric vehicle (EV) chargers and related assets.

• Responsibilities:

  • Design data models that track and report on asset health, performance, and lifecycle.

  • Collaborate with stakeholders to define key data objects for asset reliability and ensure they align with business requirements.

  • Implement processes for collecting and analyzing asset performance data to drive decisions on maintenance and operational strategies.

Implement and Maintain Data Quality Checks and Monitoring Systems

• Objective: Ensure the reliability, accuracy, and integrity of the data by implementing robust monitoring and validation mechanisms.

• Responsibilities:

  • Build and manage data quality checks that validate incoming data and ensure it meets predefined standards.

  • Develop automated monitoring systems to detect anomalies or issues in the data pipeline.

  • Establish regular data quality assessments and…