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Job Description & How to Apply Below
Data Analyst - Autonomous Vehicle (AV) Operations & Infrastructure
Role: Data Analyst – AV Support Data Infrastructure & Analytics
Experience:
4–6 Years
Location:
Remote / Flexible
Industry: Global Tech & Mobility Leader
About the Team & Opportunity
Our team owns the end-to-end data foundation driving autonomous vehicle (AV) support operations for one of the world's leading mobility platforms. Every operational metric, real-time rider contact, automated data-quality check, and pre-launch feature instrumentation flows through the systems we architect.
We sit directly at the intersection of AV Program Teams , Operational Analytics , and Core Data Engineering to turn complex operational data into actionable, trustworthy insights.
What You’ll Do
Redefine the Core Data Layer: Transform organically grown data environments into a deliberate, clean "gold layer." Consolidate overlapping tables, optimize upstream join logic, and establish unified canonical datasets.
Deliver Business Analytics & Insights: Partner with AV program leadership to scope ambiguous business challenges, define metrics, build dashboards, and answer high-impact operational questions.
Guide Instrumentation for New AV Products: Review Product Requirement Documents (PRDs) across launch life cycles to ensure proper event logging and telemetry are live before new features ship.
Maintain & Govern Data
Infrastructure: Monitor daily pipelines for freshness, accuracy, and volume. Build governance frameworks around SLAs, change-management workflows, and automated incident alerting.
Scale Real-Time Analytics: Expand streaming feeds (Kafka/Flink/Pinot) to lower latency and power real-time operational notifications as incidents develop.
What You’ll Need
4–6 years in Data Analytics, Business Analytics, or Data Engineering with a focus on dataset and pipeline ownership (not just query consumption).
Advanced SQL & Distributed Computing: Hands-on experience building production transformations in Spark SQL and Hive , authoring canonical patterns that other engineers build on.
Customer Support Data Expertise: Hands-on familiarity with support operations metrics (contacts, tickets, handle time, CSAT) and the nuances of handling messy, multi-grained data.
Strong Stakeholder Communication: Proven ability to scope vague business requests, push back when framing is off, and present clear takeaways to non-technical partners.
AI Tool Integration: Practical experience using AI/agentic coding platforms to accelerate pipeline, analysis, and dashboard development with high validation rigor.
Preferred Qualifications
AV / Autonomous Systems Interest: Understanding of or genuine curiosity about how autonomous trip operations and support models work.
Semantic & Metric Layers:
Experience with dbt, LookML, or proprietary metric management systems.
Streaming & Real-Time Analytics: Familiarity with Kafka, Flink, Pinot, or real-time event processing.
Orchestration & Observability: Hands-on experience with Airflow and data observability tools.
Dashboarding & Apps: Proficiency with Tableau, Streamlit, Retool, or custom internal tool suites.
Anam Chaudhry
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