Job Description – ADAS Data & Analytics Engineer (3–5 Years)

Role Overview

We are looking for a Data & Analytics Engineer with 3–5 years of experience to work on ADAS validation, data processing, and analytics workflows. The role involves handling large-scale vehicle data, building automation pipelines, and generating insights to support decision-making.

Key Responsibilities

Data Processing & Analysis

• Work with ADAS/event-based datasets from multiple sources (signals, logs, video-derived data)
• Perform data extraction, cleaning, and transformation using Python
• Analyze time-series data and derive meaningful insights for validation use cases

Automation & Workflow Development

• Develop scripts and workflows for automated data processing and reporting
• Identify opportunities to automate repetitive analytics tasks using ML or rule-based logic
• Work with orchestration tools (Airflow/Flyte or similar) for pipeline execution

Data Engineering & Cloud

• Handle large datasets (e.g., Parquet) and optimize data pipelines for performance
• Work with cloud platforms (preferably GCP/AWS) for data storage and processing
• Integrate APIs and databases for data access and processing

Visualization & Reporting

• Build dashboards using Power BI / Tableau / similar tools
• Design KPIs and visualize insights for stakeholders
• Ensure clear and structured storytelling of data insights

Collaboration & Domain Work

• Work closely with validation teams and SMEs to understand data requirements
• Support ADAS function analysis and validation workflows
• Contribute to continuous improvement of analytics use cases

Required Skills

Technical Skills

• Strong Python skills (Pandas, NumPy, data processing)
• Good SQL knowledge (RDBMS/NoSQL basics)
• Experience with large-scale data handling and processing
• Exposure to cloud environments (GCP/AWS preferred)
• Knowledge of dashboarding tools (Power BI/Grafana/Tableau)

Good to Have

• Experience in ADAS / automotive domain
• Understanding of time-series data & signal processing
• Exposure to ML basics (classification, model usage)
• Knowledge of containerization (Docker/Kubernetes)

Qualifications

• Bachelor’s/Master’s in Computer Science, Electronics, or related field
• 3–5 years of relevant experience in Data Analytics / Data Engineering

Behavioral Expectations

• Strong analytical and problem-solving skills
• Ability to work in a dynamic environment
• Good collaboration and communication skills
• Structured thinking and ownership mindset

• Bachelor’s/Master’s in Computer Science, Electronics, or related field
• 3–5 years of relevant experience in Data Analytics / Data Engineering