Director, Data Platform & AI

Reports To

VP Engineering

Role Overview

As the Director Data Platform & AI you will set the vision and strategy for ChargePoints data and AI platforms and lead multidisciplinary teams (Data Engineering Analytics Classical ML/Data Science and Gen/Agentic AI) to deliver innovative customercentric 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 ChargePoints transformation into an AIfirst company.

What You Will Be Doing

• Vision & Strategy: Define a 23 year Data & AI platform vision and investment plan that aligns with ChargePoints product portfolio and growth goals; articulate northstar outcomes KPIs and staged milestones for platform products and teams.
• Product Ideation & Roadmapping: Work with Product Management to ideate AIpowered 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 nearterm product impact.
• Platform Architecture: Own endtoend 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 costefficient 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 (retrievalaugmented generation toolusing agents workflow automation) with measurable impact; set patterns for prompt engineering safety evaluation and humanintheloop 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.
• CrossFunctional 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 practicesCI/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 timetoinsight 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: 1520 years across Data Engineering Analytics and Data Science including 5 years leading platform/AI teams at Director level delivering productiongrade 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 tradeoffs 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: Handson experience with classical ML (forecasting anomaly detection optimization) and Gen/Agentic AI (LLMs RAG toolcalling 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 tradeoffs data governance privacy/security by design and compliance in regulated environments.
• Influence & Communication: Executiveready 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 highperforming missiondriven teams.
• Customer Obsession: Relentless focus on driver fleet and partner experiencestranslating insights into features that differentiate ChargePoint.

Requirements

• Bachelors or Masters in Computer Science Data Science Electrical/Computer Engineering or related field.
• 1520 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.
• 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).
• 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 largescale operational systems.
• Prior ownership of feature stores model catalog/registry prompt/policy libraries and AI safety/evaluation tooling.
• Track record of cost optimization and performance tuning for large data platforms (e.g. caching strategies materialized views workload management storage tiering).
• Familiarity with multicloud connectors vector databases and enterprise LLM integration patterns.

Location

Bangalore India