Director, Data Platform & AI

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.

Requirements

• 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.
• 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 large‑scale 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 multi‑cloud connectors, vector databases, and enterprise LLM integration patterns.

Location

Bangalore, India