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:

1. Minimum 10 years of overall experience in data engineering, data platforms, or related technology roles.
2. At least 8 years of hands-on experience in data engineering, including large-scale data processing, data integration, and cloud-based data platforms
3. At least 3 years of experience(5 years ideal) in the automotive domain, preferably commercial vehicles, trucks, connected vehicles, or vehicle telemetry data.
4. Demonstrated ability to translate business and domain requirements into scalable, reliable, and maintainable data engineering architectures.

Key Responsibilities:

1. Define and maintain the end-to-end data engineering architecture for automotive and connected-vehicle data platforms.
2. Design scalable and efficient data pipelines for data ingestion, transformation, processing, storage, and consumption.
3. Define appropriate data architecture patterns, including data lake/lakehouse architectures and layered data processing approaches.
4. Work with vehicle telemetry, logged vehicle parameters, diagnostic trouble codes (DTCs), vehicle status information, and other operational vehicle data.
5. Understand the relationship between vehicle signals, ECUs, vehicle configurations, software versions, and associated metadata.
6. Architect data ingestion and transformation pipelines using technologies such as Python, SQL, PySpark, and distributed data processing frameworks.
7. Design and optimize ETL/ELT workflows .
8. Define data storage, partitioning, schema management, incremental processing, and data lifecycle strategies.

Key Skills:

1. Significant experience in data engineering, solution architecture, or technical leadership roles.
2. Strong proficiency in Python, SQL, PySpark, and distributed data processing.
3. Hands-on experience designing and implementing large-scale data pipelines and data platforms.
4. Strong understanding of data modelling, ETL/ELT, data integration, performance optimization, and data quality engineering.

Preferred Skills:

1. Experience in the automotive industry, preferably commercial vehicles or trucks.
2. Experience working with vehicle-generated time-series or telemetry data at scale.
3. 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:

1. Minimum 10 years of overall experience in data engineering, data platforms, or related technology roles.
2. At least 8 years of hands-on experience in data engineering, including large-scale data processing, data integration, and cloud-based data platforms
3. At least 3 years of experience(5 years ideal) in the automotive domain, preferably commercial vehicles, trucks, connected vehicles, or vehicle telemetry data.
4. Demonstrated ability to translate business and domain requirements into scalable, reliable, and maintainable data engineering architectures.

Key Responsibilities:

1. Define and maintain the end-to-end data engineering architecture for automotive and connected-vehicle data platforms.
2. Design scalable and efficient data pipelines for data ingestion, transformation, processing, storage, and consumption.
3. Define appropriate data architecture patterns, including data lake/lakehouse architectures and layered data processing approaches.
4. Work with vehicle telemetry, logged vehicle parameters, diagnostic trouble codes (DTCs), vehicle status information, and other operational vehicle data.
5. Understand the relationship between vehicle signals, ECUs, vehicle configurations, software versions, and associated metadata.
6. Architect data ingestion and transformation pipelines using technologies such as Python, SQL, PySpark, and distributed data processing frameworks.
7. Design and optimize ETL/ELT workflows .
8. Define data storage, partitioning, schema management, incremental processing, and data lifecycle strategies.

Key Skills:

1. Significant experience in data engineering, solution architecture, or technical leadership roles.
2. Strong proficiency in Python, SQL, PySpark, and distributed data processing.
3. Hands-on experience designing and implementing large-scale data pipelines and data platforms.
4. Strong understanding of data modelling, ETL/ELT, data integration, performance optimization, and data quality engineering.

Preferred Skills:

1. Experience in the automotive industry, preferably commercial vehicles or trucks.
2. Experience working with vehicle-generated time-series or telemetry data at scale.
3. 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:

1. Minimum 10 years of overall experience in data engineering, data platforms, or related technology roles.
2. At least 8 years of hands-on experience in data engineering, including large-scale data processing, data integration, and cloud-based data platforms
3. At least 3 years of experience(5 years ideal) in the automotive domain, preferably commercial vehicles, trucks, connected vehicles, or vehicle telemetry data.
4. Demonstrated ability to translate business and domain requirements into scalable, reliable, and maintainable data engineering architectures.

Key Responsibilities:

1. Define and maintain the end-to-end data engineering architecture for automotive and connected-vehicle data platforms.
2. Design scalable and efficient data pipelines for data ingestion, transformation, processing, storage, and consumption.
3. Define appropriate data architecture patterns, including data lake/lakehouse architectures and layered data processing approaches.
4. Work with vehicle telemetry, logged vehicle parameters, diagnostic trouble codes (DTCs), vehicle status information, and other operational vehicle data.
5. Understand the relationship between vehicle signals, ECUs, vehicle configurations, software versions, and associated metadata.
6. Architect data ingestion and transformation pipelines using technologies such as Python, SQL, PySpark, and distributed data processing frameworks.
7. Design and optimize ETL/ELT workflows .
8. Define data storage, partitioning, schema management, incremental processing, and data lifecycle strategies.

Key Skills:

1. Significant experience in data engineering, solution architecture, or technical leadership roles.
2. Strong proficiency in Python, SQL, PySpark, and distributed data processing.
3. Hands-on experience designing and implementing large-scale data pipelines and data platforms.
4. Strong understanding of data modelling, ETL/ELT, data integration, performance optimization, and data quality engineering.

Preferred Skills:

1. Experience in the automotive industry, preferably commercial vehicles or trucks.
2. Experience working with vehicle-generated time-series or telemetry data at scale.
3. Familiarity with Databricks, Delta Lake, Azure data services, or equivalent cloud data engineering technologies.

Other Requirements