{"id":3150359,"url":"https://alion.io/job/hcltech-data-architect-2","title":"Data Architect","company":{"id":225,"name":"HCLTech","domain":"hcltech.com","url":"https://alion.io/company/hcltech","size_band":"5000+","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"SuccessFactors","truth_index":null},"role":"Data Science","role_family":"Data Science","seniority":"staff","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Bengaluru, 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Architect - Data Engineering\n\nData Engineering\n\ndata engineering, solution architecture, automotive domain, Python, SQL, PySpark\n\nDatabricks, Delta Lake, Azure data services\n\nJD for technical Architect:\nExperience required:\nMinimum 10 years of overall experience in data engineering, data platforms, or related technology roles.\nAt least 8 years of hands-on experience in data engineering, including large-scale data processing, data integration, and cloud-based data platforms\nAt least 3 years of experience(5 years ideal) in the automotive domain, preferably commercial vehicles, trucks, connected vehicles, or vehicle telemetry data.\nDemonstrated ability to translate business and domain requirements into scalable, reliable, and maintainable data engineering architectures.\nKey Responsibilities:\nDefine and maintain the end-to-end data engineering architecture for automotive and connected-vehicle data platforms.\nDesign scalable and efficient data pipelines for data ingestion, transformation, processing, storage, and consumption.\nDefine appropriate data architecture patterns, including data lake/lakehouse architectures and layered data processing approaches.\nWork with vehicle telemetry, logged vehicle parameters, diagnostic trouble codes (DTCs), vehicle status information, and other operational vehicle data.\nUnderstand the relationship between vehicle signals, ECUs, vehicle configurations, software versions, and associated metadata.\nArchitect data ingestion and transformation pipelines using technologies such as Python, SQL, PySpark, and distributed data processing frameworks.\nDesign and optimize ETL/ELT workflows .\nDefine data storage, partitioning, schema management, incremental processing, and data lifecycle strategies.\nKey Skills:\nSignificant experience in data engineering, solution architecture, or technical leadership roles.\nStrong proficiency in Python, SQL, PySpark, and distributed data processing.\nHands-on experience designing and implementing large-scale data pipelines and data platforms.\nStrong understanding of data modelling, ETL/ELT, data integration, performance optimization, and data quality engineering.\nPreferred Skills:\nExperience in the automotive industry, preferably commercial vehicles or trucks.\nExperience working with vehicle-generated time-series or telemetry data at scale.\nFamiliarity with Databricks, Delta Lake, Azure data services, or equivalent cloud data engineering technologies.\nKey Responsibilities\n\nRole Name\n\nSkill Category\n\nSkillset Keywords\n\nTechnical Architect - Data Engineering\n\nData Engineering\n\ndata engineering, solution architecture, automotive domain, Python, SQL, PySpark\n\nDatabricks, Delta Lake, Azure data services\n\nJD for technical Architect:\nExperience required:\nMinimum 10 years of overall experience in data engineering, data platforms, or related technology roles.\nAt least 8 years of hands-on experience in data engineering, including large-scale data processing, data integration, and cloud-based data platforms\nAt least 3 years of experience(5 years ideal) in the automotive domain, preferably commercial vehicles, trucks, connected vehicles, or vehicle telemetry data.\nDemonstrated ability to translate business and domain requirements into scalable, reliable, and maintainable data engineering architectures.\nKey Responsibilities:\nDefine and maintain the end-to-end data engineering architecture for automotive and connected-vehicle data platforms.\nDesign scalable and efficient data pipelines for data ingestion, transformation, processing, storage, and consumption.\nDefine appropriate data architecture patterns, including data lake/lakehouse architectures and layered data processing approaches.\nWork with vehicle telemetry, logged vehicle parameters, diagnostic trouble codes (DTCs), vehicle status information, and other operational vehicle data.\nUnderstand the relationship between vehicle signals, ECUs, vehicle configurations, software versions, and associated metadata.\nArchitect data ingestion and transformation pipelines using technologies such as Python, SQL, PySpark, and distributed data processing frameworks.\nDesign and optimize ETL/ELT workflows .\nDefine data storage, partitioning, schema management, incremental processing, and data lifecycle strategies.\nKey Skills:\nSignificant experience in data engineering, solution architecture, or technical leadership roles.\nStrong proficiency in Python, SQL, PySpark, and distributed data processing.\nHands-on experience designing and implementing large-scale data pipelines and data platforms.\nStrong understanding of data modelling, ETL/ELT, data integration, performance optimization, and data quality engineering.\nPreferred Skills:\nExperience in the automotive industry, preferably commercial vehicles or trucks.\nExperience working with vehicle-generated time-series or telemetry data at scale.\nFamiliarity with Databricks, Delta Lake, Azure data services, or equivalent cloud data engineering technologies.\nSkill Requirements\n\nRole Name\n\nSkill Category\n\nSkillset Keywords\n\nTechnical Architect - Data Engineering\n\nData Engineering\n\ndata engineering, solution architecture, automotive domain, Python, SQL, PySpark\n\nDatabricks, Delta Lake, Azure data services\n\nJD for technical Architect:\nExperience required:\nMinimum 10 years of overall experience in data engineering, data platforms, or related technology roles.\nAt least 8 years of hands-on experience in data engineering, including large-scale data processing, data integration, and cloud-based data platforms\nAt least 3 years of experience(5 years ideal) in the automotive domain, preferably commercial vehicles, trucks, connected vehicles, or vehicle telemetry data.\nDemonstrated ability to translate business and domain requirements into scalable, reliable, and maintainable data engineering architectures.\nKey Responsibilities:\nDefine and maintain the end-to-end data engineering architecture for automotive and connected-vehicle data platforms.\nDesign scalable and efficient data pipelines for data ingestion, transformation, processing, storage, and consumption.\nDefine appropriate data architecture patterns, including data lake/lakehouse architectures and layered data processing approaches.\nWork with vehicle telemetry, logged vehicle parameters, diagnostic trouble codes (DTCs), vehicle status information, and other operational vehicle data.\nUnderstand the relationship between vehicle signals, ECUs, vehicle configurations, software versions, and associated metadata.\nArchitect data ingestion and transformation pipelines using technologies such as Python, SQL, PySpark, and distributed data processing frameworks.\nDesign and optimize ETL/ELT workflows .\nDefine data storage, partitioning, schema management, incremental processing, and data lifecycle strategies.\nKey Skills:\nSignificant experience in data engineering, solution architecture, or technical leadership roles.\nStrong proficiency in Python, SQL, PySpark, and distributed data processing.\nHands-on experience designing and implementing large-scale data pipelines and data platforms.\nStrong understanding of data modelling, ETL/ELT, data integration, performance optimization, and data quality engineering.\nPreferred Skills:\nExperience in the automotive industry, preferably commercial vehicles or trucks.\nExperience working with vehicle-generated time-series or telemetry data at scale.\nFamiliarity with Databricks, Delta Lake, Azure data services, or equivalent cloud data engineering technologies.\nOther 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