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
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Work with ADAS/event-based datasets from multiple sources (signals, logs, video-derived data)
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Perform data extraction, cleaning, and transformation using Python
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Analyze time-series data and derive meaningful insights for validation use cases
Automation & Workflow Development
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Develop scripts and workflows for automated data processing and reporting
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Identify opportunities to automate repetitive analytics tasks using ML or rule-based logic
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Work with orchestration tools (Airflow/Flyte or similar) for pipeline execution
Data Engineering & Cloud
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Handle large datasets (e.g., Parquet) and optimize data pipelines for performance
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Work with cloud platforms (preferably GCP/AWS) for data storage and processing
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Integrate APIs and databases for data access and processing
Visualization & Reporting
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Build dashboards using Power BI / Tableau / similar tools
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Design KPIs and visualize insights for stakeholders
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Ensure clear and structured storytelling of data insights
Collaboration & Domain Work
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Work closely with validation teams and SMEs to understand data requirements
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Support ADAS function analysis and validation workflows
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Contribute to continuous improvement of analytics use cases
Required Skills
Technical Skills
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Strong Python skills (Pandas, NumPy, data processing)
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Good SQL knowledge (RDBMS/NoSQL basics)
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Experience with large-scale data handling and processing
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Exposure to cloud environments (GCP/AWS preferred)
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Knowledge of dashboarding tools (Power BI/Grafana/Tableau)
Good to Have
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Experience in ADAS / automotive domain
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Understanding of time-series data & signal processing
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Exposure to ML basics (classification, model usage)
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Knowledge of containerization (Docker/Kubernetes)
Qualifications
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Bachelor’s/Master’s in Computer Science, Electronics, or related field
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3–5 years of relevant experience in Data Analytics / Data Engineering
Behavioral Expectations
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Strong analytical and problem-solving skills
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Ability to work in a dynamic environment
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Good collaboration and communication skills
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Structured thinking and ownership mindset
