ADAS Data & Analytics Engineer
Mercedes-Benz Group AG
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Job summary
Mercedes-Benz Group AG in Bengaluru seeks an experienced Data & Analytics Engineer (4–7 years) to drive ADAS validation, data processing, and analytics workflows. You will process large vehicle datasets, build automation pipelines, and derive insights to support decision-making.
The role emphasizes data engineering in cloud environments (GCP/AWS) and creating dashboards with Power BI/Tableau, along with collaboration with validation teams to improve analytics use cases.
Qualifications
- 4–7 years of experience in Data Analytics / Data Engineering.
- Proficient in Python for data processing and Pandas/NumPy.
- Experience with cloud platforms (GCP/AWS) and SQL basics.
- Experience building dashboards with Power BI/Tableau/Grafana.
Responsibilities
- Data Processing & Analysis: work with ADAS datasets, perform extraction, cleaning, and transformation using Python.
- Automation & Workflow Development: create scripts and workflows for automated data processing and reporting with orchestration tools.
- Data Engineering & Cloud: optimize pipelines, handle large datasets (Parquet), use cloud storage/processing.
- Visualization & Reporting: build dashboards and design KPIs for stakeholders.
- Collaboration & Domain Work: align with validation teams and SMEs on data requirements.
Skills
Education
Tools
Job description
Job Description – ADAS Data & Analytics Engineer (4–7 Years)
Role Overview
We are looking for a Data & Analytics Engineer with 4–7 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
- 4–7 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
Job Description – ADAS Data & Analytics Engineer (4–7 Years)
Role Overview
We are looking for a Data & Analytics Engineer with 4–7 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
- 4–7 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
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