Data Architect

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 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.

Other Requirements