About Us

With electric vehicles expected to be nearly 30% of new vehicle sales by 2025 and more than 50% by 2040, electric mobility is becoming a reality. ChargePoint (NYSE: CHPT) is at the center of this revolution, powering one of the world’s leading EV charging networks and a comprehensive set of hardware, software and mobile solutions for every charging need across North America and Europe. We bring together drivers, businesses, automakers, policymakers, utilities and other stakeholders to make e-mobility a global reality.

Since our founding in 2007, ChargePoint has focused solely on making the transition to electric easy for businesses, fleets and drivers. ChargePoint offers a once-in-a-lifetime opportunity to create an all-electric future and a trillion-dollar market.

At ChargePoint, we foster a positive and productive work environment by committing to live our values of Be Courageous, Charge Together, Love our Customers, Operate with Openness, and Relentlessly Pursue Awesome. These values guide how we show up every day, align, and work together to build a brighter future for all of us.

• Join the team that is building the EV charging industry and make your mark on how people and goods will get everywhere they need to go, in any context, for generations to come. Reports To

VP, Engineering

Role Overview

As the Director, Data Platform & AI , you will set the vision and strategy for ChargePoint’s data and AI platforms, and lead multi‑disciplinary teams (Data Engineering, Analytics, Classical ML/Data Science, and Gen/Agentic AI) to deliver innovative, customer‑centric data and AI features at global scale. You will partner closely with Product, Software, Hardware, Cloud/SRE, Security, and Business stakeholders to translate data product and feature requirements into roadmaps, architectures, and outcomes that accelerate ChargePoint’s transformation into an AI‑first company .

• What You Will Be Doing
• Vision & Strategy: Define a 2–3 year Data & AI platform vision and investment plan that aligns with ChargePoint’s product portfolio and growth goals; articulate north‑star outcomes, KPIs, and staged milestones for platform, products, and teams.

Product Ideation & Roadmapping: Work with Product Management to ideate AI‑powered features (e.g., intelligent pricing and load management, driver experience personalization, fleet insights, predictive maintenance, customer support automation), shape PRDs, and prioritize a balanced roadmap across foundational platform work and near‑term product impact.

Platform Architecture: Own end‑to‑end architecture for the data platform (ingestion, storage, transformation, governance, serving, observability) and AI stack (feature stores, model registry, evaluation, MLOps, prompt/policy libraries, RAG infrastructure, agent orchestration, guardrails).

Execution Leadership: Lead multiple squads to deliver reliable, secure, cost‑efficient data services and AI capabilities; establish engineering excellence (design reviews, architecture standards, SLAs/SLOs, incident playbooks, cost and performance baselines).

Gen AI & Agentic AI: Drive applied use cases of LLMs and agent frameworks (retrieval‑augmented generation, tool‑using agents, workflow automation) with measurable impact; set patterns for prompt engineering, safety, evaluation, and human‑in‑the‑loop controls.

• Mentorship & Talent Development: Build, coach, and mentor talent across Data Engineering, Analytics, ML/DS, and GenAI; grow emerging leaders; foster a culture of curiosity, accountability, and continuous learning.
• Cross‑Functional Partnering: Proactively connect with engineering leaders across software, cloud, security, and QA to align designs and delivery; remove blockers and ensure seamless integration of data and AI features in customer experiences.
• Data Governance & Compliance: Champion data quality, lineage, cataloging, privacy/security, and responsible AI practices (model documentation, bias/effectiveness monitoring, PII handling, policy compliance).
• Operational Excellence: Implement robust MLOps/DataOps practices—CI/CD for pipelines and models, canary deployments, drift detection, model monitoring, cost observability, reliability engineering for data services.
• Outcome Ownership: Tie platform and AI investments to business outcomes (e.g., reduced time‑to‑insight, improved charger uptime, lower support costs, increased driver conversion); communicate results and next steps to executives and stakeholders.
• What You Will Bring to ChargePoint
• Proven Leadership: 15–20 years across Data Engineering, Analytics, and Data Science, including 5+ years leading platform/AI teams at Director+ level, delivering production‑grade data and AI capabilities at scale.
• Strategic Product Mindset: Ability to translate ambiguous business needs into clear data/AI opportunities, create compelling product visions, and drive prioritization and trade‑offs with Product and Engineering.
• Deep Technical Breadth:

Data Platform: Expertise with cloud data stacks (preferably AWS), including streaming (Kafka/Kinesis), orchestration (Airflow), transformation (Spark/PySpark, dbt), warehousing/lakehouse (Snowflake/Databricks), and infrastructure as code (Terraform).

• Analytics & BI: Scalable semantic modeling, KPI frameworks, and enablement with tools like Power BI or Tableau.
• ML/AI: Hands‑on experience with classical ML (forecasting, anomaly detection, optimization) and Gen/Agentic AI (LLMs, RAG, tool‑calling agents, safety/guardrails, evaluation and observability).
• MLOps/DataOps: CI/CD for data and models, feature stores, model registry, automated testing, lineage/metadata, monitoring, and rollback/retention strategies.
• Architecture & Security: Strong grasp of distributed systems, cost/performance trade‑offs, data governance, privacy/security by design, and compliance in regulated environments.
• Influence & Communication: Executive‑ready communication; ability to align diverse stakeholders, drive decisions, and clearly narrate technical strategy and business impact.
• People Leadership: Inclusive leadership style; history of mentoring engineers, analysts, and data scientists; building high‑performing, mission‑driven teams.
• Customer Obsession: Relentless focus on driver, fleet, and partner experiences—translating insights into features that differentiate ChargePoint.

• Bachelor’s or Master’s in Computer Science, Data Science, Electrical/Computer Engineering, or related field.
• 15–20 years of progressive experience across Data Engineering, Analytics, and Data Science; 5+ years leading data/AI platform organizations.
• Demonstrated delivery of production ML and GenAI use cases with measurable business outcomes.
• Strong proficiency in Python; familiarity with modern development practices (code reviews, design docs, testing, observability).
• Experience establishing governance, privacy/security controls, and responsible AI practices.
• Excellent communication, stakeholder management, and leadership skills.
• Preferred Qualifications
• Experience in EV, energy, IoT/edge telemetry, or large‑scale operational systems.
• Prior ownership of feature stores, model catalog/registry, prompt/policy libraries, and AI safety/evaluation tooling.
• Familiarity with multi‑cloud connectors, vector databases, and enterprise LLM integration patterns.
• Location
• Bangalore, India
• If there is a match between your experiences/skills and the Company needs, we will contact you directly.

ChargePoint is committed to fostering an inclusive workplace that welcomes and supports all qualified individuals. In alignment with this commitment, we ensure that persons with disabilities are provided with reasonable accommodations throughout the employment process.

• If you need a reasonable accommodation to participate in the application or interview process, to perform essential job functions, or to access any other benefits and privileges of employment, please contact us at accommodations@chargepoint.com .
• ChargePoint is an equal opportunity employer. Applicants only - Recruiting agencies do not contact.

• Expertise in AWS data stack (e.g., S3, Glue, EMR/Spark, Lambda, Kinesis, Redshift/Snowflake, SageMaker/Bedrock) and modern data tooling (Kafka, Airflow, dbt, Databricks, Terraform, Kubernetes).
• Track record of cost optimization and performance tuning for large data platforms (e.g., caching strategies, materialized views, workload management, storage tiering).

We are committed to an inclusive and diverse team. ChargePoint is an equal opportunity employer. We do not discriminate based on race, color, ethnicity, ancestry, national origin, religion, sex, gender, gender identity, gender expression, sexual orientation, age, disability, veteran status, genetic information, marital status or any legally protected status.