Job Summary
Role Name
Skill Category
Skillset Keywords
Technical Architect – Data Engineering
Data Engineering
data engineering, solution architecture, automotive domain, Python, SQL, PySpark
Databricks, Delta Lake, Azure data services
JD for technical Architect:
Experience required:
- Minimum 10 years of overall experience in data engineering, data platforms, or related technology roles.
- At least 8 years of hands-on experience in data engineering, including large-scale data processing, data integration, and cloud-based data platforms
- At least 3 years of experience(5 years ideal) in the automotive domain, preferably commercial vehicles, trucks, connected vehicles, or vehicle telemetry data.
- Demonstrated ability to translate business and domain requirements into scalable, reliable, and maintainable data engineering architectures.
Key Responsibilities:
- Define and maintain the end-to-end data engineering architecture for automotive and connected-vehicle data platforms.
- Design scalable and efficient data pipelines for data ingestion, transformation, processing, storage, and consumption.
- Define appropriate data architecture patterns, including data lake/lakehouse architectures and layered data processing approaches.
- Work with vehicle telemetry, logged vehicle parameters, diagnostic trouble codes (DTCs), vehicle status information, and other operational vehicle data.
- Understand the relationship between vehicle signals, ECUs, vehicle configurations, software versions, and associated metadata.
- Architect data ingestion and transformation pipelines using technologies such as Python, SQL, PySpark, and distributed data processing frameworks.
- Design and optimize ETL/ELT workflows .
- Define data storage, partitioning, schema management, incremental processing, and data lifecycle strategies.
Key Skills:
- Significant experience in data engineering, solution architecture, or technical leadership roles.
- Strong proficiency in Python, SQL, PySpark, and distributed data processing.
- Hands-on experience designing and implementing large-scale data pipelines and data platforms.
- Strong understanding of data modelling, ETL/ELT, data integration, performance optimization, and data quality engineering.
Preferred Skills:
- Experience in the automotive industry, preferably commercial vehicles or trucks.
- Experience working with vehicle-generated time-series or telemetry data at scale.
- Familiarity with Databricks, Delta Lake, Azure data services, or equivalent cloud data engineering technologies.
Key Responsibilities
Role Name
Skill Category
Skillset Keywords
Technical Architect – Data Engineering
Data Engineering
data engineering, solution architecture, automotive domain, Python, SQL, PySpark
Databricks,
Delta Lake, Azure data services
JD for technical Architect:
Experience required:
- Minimum 10 years of overall experience in data engineering, data platforms, or related technology roles.
- At least 8 years of hands-on experience in data engineering, including large-scale data processing, data integration, and cloud-based data platforms
- At least 3 years of experience(5 years ideal) in the automotive domain, preferably commercial vehicles, trucks, connected vehicles, or vehicle telemetry data.
- Demonstrated ability to translate business and domain requirements into scalable, reliable, and maintainable data engineering architectures.
Key Responsibilities:
- Define and maintain the end-to-end data engineering architecture for automotive and connected-vehicle data platforms.
- Design scalable and efficient data pipelines for data ingestion, transformation, processing, storage, and consumption.
- Define appropriate data architecture patterns, including data lake/lakehouse architectures and layered data processing approaches.
- Work with vehicle telemetry, logged vehicle parameters, diagnostic trouble codes (DTCs), vehicle status information, and other operational vehicle data.
- Understand the relationship between vehicle signals, ECUs, vehicle configurations, software versions, and associated metadata.
- Architect data ingestion and transformation pipelines using technologies such as Python, SQL, PySpark, and distributed data processing frameworks.
- Design and optimize ETL/ELT workflows .
- Define data storage, partitioning, schema management, incremental processing, and data lifecycle strategies.
Key Skills:
- Significant experience in data engineering, solution architecture, or technical leadership roles.
- Strong proficiency in Python, SQL, PySpark, and distributed data processing.
- Hands-on experience designing and implementing large-scale data pipelines and data platforms.
- Strong understanding of data modelling, ETL/ELT, data integration, performance optimization, and data quality engineering.
Preferred Skills:
- Experience in the automotive industry, preferably commercial vehicles or trucks.
- Experience working with vehicle-generated time-series or telemetry data at scale.
- Familiarity with Databricks, Delta Lake, Azure data services, or equivalent cloud data engineering technologies.
Skill Requirements
Role Name
Skill Category
Skillset Keywords
Technical Architect – Data Engineering
Data Engineering
data engineering, solution architecture, automotive domain, Python, SQL, PySpark
Databricks, Delta Lake, Azure data services
JD for technical Architect:
Experience required:
- Minimum 10 years of overall experience in data engineering, data platforms, or related technology roles.
- At least 8 years of hands-on experience in data engineering, including large-scale data processing, data integration, and cloud-based data platforms
- At least 3 years of experience(5 years ideal) in the automotive domain, preferably commercial vehicles, trucks, connected vehicles, or vehicle telemetry data.
- Demonstrated ability to translate business and domain requirements into scalable, reliable, and maintainable data engineering architectures.
Key Responsibilities:
- Define and maintain the end-to-end data engineering architecture for automotive and connected-vehicle data platforms.
- Design scalable and productive data pipelines for data ingestion, transformation, processing, storage, and consumption.
- Define appropriate data architecture patterns, including data lake/lakehouse architectures and layered data processing approaches.
- Work with vehicle telemetry, logged vehicle parameters, diagnostic trouble codes (DTCs), vehicle status information, and other operational vehicle data.
- Understand the relationship between vehicle signals, ECUs, vehicle configurations, software versions, and associated metadata.
- Architect data ingestion and transformation pipelines using technologies such as Python, SQL, PySpark, and distributed data processing frameworks.
- Design and optimize ETL/ELT workflows .
- Define data storage, partitioning, schema management, incremental processing, and data lifecycle strategies.
Key Skills:
- Significant experience in data engineering, solution architecture, or technical leadership roles.
- Strong proficiency in Python, SQL, PySpark, and distributed data processing.
- Hands-on experience designing and implementing large-scale data pipelines and data platforms.
- Strong understanding of data modelling, ETL/ELT, data integration, performance optimization, and data quality engineering.
Preferred Skills:
- Experience in the automotive industry, preferably commercial vehicles or trucks.
- Experience working with vehicle-generated time-series or telemetry data at scale.
- Familiarity with Databricks, Delta Lake, Azure data services, or equivalent cloud data engineering technologies.
Must Have Skill
Data Architecture,MS SQL,PySpark,Python,Solution Architecture,Automotive
Good to Have Skill
Other Requirements
📌 Data Architect (India)
🏢 HCLTech
📍 India