Senior Data & Backend Engineer - AI-Driven Platform

Chargie LLC

Culver City (CA)

Hybrid

USD 140,000 - 210,000

Full time

14 days+

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Job summary

Chargie LLC is seeking a Senior Data & Backend Engineer to stand up and support a data platform powering analytics, BI reporting, and operational visibility for one of the fastest-growing EV charging networks in the country. The role combines data engineering with backend development and offers hands-on work on APIs, GraphQL, and REST services.

The position is hybrid, with opportunities to influence AI-assisted tooling and cross-functional collaboration across engineering, product, and

Qualifications

Bachelor's degree in Computer Science, Data Engineering, or related field.
5+ years of experience spanning data engineering and backend software engineering.
Strong SQL skills and hands-on experience with relational databases, data modeling, schema design, and ETL/data pipeline development.

Responsibilities

Design, build, and support scalable data pipelines and big-data platform infrastructure, from ingestion through transformation to reporting layers.
Own data architecture and modeling decisions that support analytics, BI reporting, and operational tooling across the organization.
Build and maintain BI/reporting layers and dashboards for internal stakeholders and leadership, using tools such as Power BI (Microsoft Fabric experience a plus).
Step into Node.js backend development and production support as needed — contributing to internal GraphQL APIs and RESTful/event-driven services alongside the core engineering team.
Design and maintain integrations with third‑party platforms, vendor APIs, and IoT/OCPP-based EV charging infrastructure systems.
Build and support integrations with business systems (e.g., Zoho, Atlassian, NetSuite) to connect engineering, operations, and business data.
Support external reporting to vendors and utilities as this area of demand grows, providing additional coverage alongside the engineering team (a plus).
Support and improve AWS infrastructure relevant to data and backend services, including deployment pipelines, monitoring, and cost optimization.
Partner with engineering leads, product, and operations teams to define and deliver features supporting network management, customer experience, and enterprise integrations.
Establish and uphold quality standards including code review, documentation, testing practices, and CI/CD integration across both data and backend workstreams.
Leverage AI‑assisted development tools (e.g., Claude, Cursor AI) to accelerate development velocity, improve code quality, and explore AI‑powered capabilities within the product.