Databricks Jobs

Quality Data Analyst – Electrical Hardware EngineeringWarranty Analytics | KPI Automation | Design Assessment | AI Adoption

RLE India Private Limited•

Chennai

•full time

Job Summary We are seeking a skilled and experienced Quality Data Analyst to join our Electrical Hardware Engineering team. This role will focus on Warranty Analytics, KPI Automation, Design Assessment, and the adoption of AI within our quality processes. The ideal candidate will possess a strong analytical background and a passion for leveraging data to drive improvements in product quality and operational efficiency. This is an onsite position. Key Responsibilities The Quality Data Analyst will be responsible for a range of tasks including: Performing Data Analysis to identify trends and patterns in warranty data, product performance, and manufacturing processes. Developing and maintaining KPI Reporting dashboards to track key quality metrics and provide actionable insights to stakeholders. Automating KPI generation and reporting processes to improve efficiency and accuracy. Conducting Root-Cause Analysis of quality issues and collaborating with engineering teams to implement effective Corrective Action Management plans. Utilizing Data Mining techniques to uncover hidden patterns and insights within large datasets. Applying Statistical Analysis methods to evaluate product performance and identify areas for improvement. Leveraging AI-Assisted Analytics and Generative AI to enhance data analysis capabilities and identify potential quality risks. Implementing Process Optimization strategies to streamline quality control processes and improve overall efficiency. Required Skills & Qualifications To be successful in this role, candidates must possess the following skills and qualifications: A Bachelor’s degree in Electrical Engineering, Electronics Engineering, Automotive Engineering, Data Analytics, Computer Science, or a related engineering discipline. 5-10 years of experience in a data analysis role, preferably within the automotive or technology industry. This level of experience indicates a proven ability to independently manage complex data analysis projects and deliver impactful insights. Data Analysis : A strong foundation in data analysis techniques, including data cleaning, transformation, and exploration. Data Visualization : Proficiency in creating compelling and informative data visualizations to communicate insights effectively. Dashboarding : Experience in designing and developing interactive dashboards using tools such as Power BI . KPI Reporting : Ability to define, track, and report on key performance indicators ( KPIs ) to measure quality performance. Data Storytelling : Skill in translating complex data into clear and concise narratives that resonate with stakeholders. Quality Tools : Familiarity with various quality tools and methodologies, such as Lean and Six Sigma . Issue Management : Experience in identifying, tracking, and resolving quality issues in a timely and effective manner. Root-Cause Analysis : Ability to conduct thorough root-cause analyses to identify the underlying causes of quality problems. Corrective Action Management : Experience in developing and implementing corrective action plans to prevent recurrence of quality issues. Process Optimization : Ability to identify and implement process improvements to enhance quality control processes. Power BI : Proficiency in using Power BI to create interactive dashboards and reports. Excel Analytics : Advanced skills in Excel for data analysis and reporting. SQL : Strong proficiency in SQL for querying and manipulating data. Python : Experience using Python for data analysis and automation. Databricks : Familiarity with Databricks for big data processing and analysis. Power Automate : Experience using Power Automate for automating data-related tasks. Warranty Data Analytics : Experience analyzing warranty data to identify trends and patterns. Cost Trend Analysis : Ability to analyze cost trends to identify opportunities for cost savings. KPI Automation : Experience automating KPI generation and reporting processes. Multi-Source Data Integration : Ability to integrate data from multiple sources into a unified view. AI-Assisted Analytics : Experience leveraging AI to enhance data analysis capabilities. Predictive Quality : Ability to use data to predict potential quality issues. Anomaly Detection : Skill in identifying unusual patterns or outliers in data. Generative AI : Familiarity with Generative AI techniques for data analysis and insights. Process Automation : Experience automating manual processes to improve efficiency. Data Mining : Ability to extract valuable insights from large datasets. Statistical Analysis : Proficiency in applying statistical methods to analyze data. Quality Metrics : Understanding of key quality metrics and their application. Data Definitions : Ability to define clear and consistent data definitions. What We Offer We offer a competitive salary of .

Quality Data Analyst – Electrical Hardware Engineer

RLE India Private Limited•

Chennai

•full time

Job Summary We are seeking a skilled and experienced Quality Data Analyst to join our Electrical Hardware Engineering, Warranty Analytics & Quality Automation team. This role will be instrumental in leveraging data to drive quality improvements, identify trends, and ensure the reliability of our products. The ideal candidate will possess a strong analytical background, a passion for data-driven decision-making, and a commitment to continuous improvement within the automotive and technology industries. Key Responsibilities The Quality Data Analyst will be responsible for a range of tasks related to data analysis, reporting, and process improvement. This includes conducting in-depth analysis of warranty data, identifying cost trends, and developing automated reporting solutions. The role will also involve collaborating with cross-functional teams to implement corrective actions and preventative measures to enhance product quality and reliability. Required Skills & Qualifications To be successful in this role, candidates must possess a combination of technical skills, analytical abilities, and a strong understanding of quality methodologies. Specific requirements include: Analytical Skills: Demonstrated ability to analyze complex data sets, identify patterns, and draw meaningful conclusions. Problem-Solving Skills: Proven ability to approach complex problems systematically, identify root causes, and develop effective solutions. Data Analysis: Proficiency in data analysis techniques, including statistical analysis and data mining. Data Visualization: Ability to create clear and compelling data visualizations to communicate insights effectively. Dashboarding: Experience in designing and developing interactive dashboards to monitor key performance indicators (KPIs). KPI Reporting: Ability to generate regular reports on KPIs and present findings to stakeholders. Data Storytelling: Ability to translate data into a narrative that is easy to understand and actionable. SQL: Strong proficiency in SQL for data extraction, manipulation, and analysis. Python: Experience using Python for data analysis and automation. Databricks: Familiarity with Databricks for large-scale data processing and analytics. Power Automate: Experience with Power Automate for automating data-related tasks and workflows. Warranty Data Analytics: Expertise in analyzing warranty data to identify trends and opportunities for improvement. Cost Trend Analysis: Ability to analyze cost data to identify trends and potential cost savings. KPI Automation: Experience in automating the generation and distribution of KPIs. Multi-Source Data Integration: Ability to integrate data from multiple sources into a unified view. AI-Assisted Analytics: Experience leveraging AI to enhance data analysis and predictive modeling. Predictive Quality: Knowledge of predictive quality techniques to anticipate and prevent quality issues. Anomaly Detection: Ability to identify unusual patterns or outliers in data that may indicate quality problems. Generative AI: Familiarity with Generative AI techniques for data analysis and problem-solving. Issue Management: Experience in tracking and resolving quality issues. Root-Cause Analysis: Proficiency in conducting root-cause analysis to identify the underlying causes of quality problems. Corrective Action Management: Ability to develop and implement effective corrective actions to prevent recurrence of quality issues. DFMEA: Understanding of DFMEA (Design Failure Mode and Effects Analysis) principles. DRBFM: Understanding of DRBFM (Design Review Based on Failure Mode) principles. APQP: Understanding of APQP (Advanced Product Quality Planning) principles. Statistical Tools: Proficiency in using various statistical tools for data analysis and quality improvement. Structured Problem Solving: Ability to apply structured problem-solving methodologies to address quality challenges. Power BI: Proficiency in Power BI for data visualization and reporting. Excel Analytics: Strong Excel skills for data analysis and reporting. Process Optimization: Ability to identify and implement process improvements to enhance quality and efficiency. Six Sigma: Knowledge of Six Sigma methodologies for process improvement. Quality Tools: Familiarity with various quality tools and techniques. Lean: Understanding of Lean principles for waste reduction and process improvement. Education: A Bachelor’s degree in Electrical Engineering, Electronics Engineering, Automotive Engineering, Data Analytics, Computer Science, or a related engineering discipline is required. Experience: The ideal candidate will have 5-10 years of experience in a related field, demonstrating a strong track record of success in data analysis and quality improvement. This level of experience is expected to provide a solid foundation for independent work and leadership within a team. What We Offer Salary:

Azure Cloud & Data ETL Developer

SACHA Engineering•

Bengaluru

•full time

Job Summary We are seeking a skilled and experienced Azure Cloud & Data ETL Developer to join our dynamic IT / Technology team. This is a hybrid role, offering a blend of remote work and in-office collaboration. The ideal candidate will possess a strong background in data engineering, cloud technologies, and ETL/ELT development, with a focus on the Azure ecosystem. Key Responsibilities The Azure Cloud & Data ETL Developer will be responsible for designing, developing, and maintaining robust and scalable data pipelines and solutions within the Azure cloud environment. This includes data ingestion, transformation, and loading processes, ensuring data quality, and adhering to data governance principles. The role also involves collaborating with cross-functional teams to understand data requirements and deliver effective solutions. Required Skills & Qualifications Azure Data Services: Demonstrated proficiency in utilizing various Azure data services, including Azure Data Factory (ADF) , Azure Databricks , and Azure Synapse Analytics , to build and manage data pipelines. ETL Development: Extensive experience in ETL (Extract, Transform, Load) and ELT (Extract, Load, Transform) development, including designing and implementing data integration solutions. Hybrid Data Architectures: A strong understanding of hybrid data architectures , enabling seamless data flow between on-premises and cloud environments. Cloud Migration: Experience in migrating data and applications to the Azure cloud platform. Data Engineering: Solid foundation in Data Engineering principles and practices. Data Modeling: Expertise in data modeling techniques, including the design and implementation of star schemas and snowflake schemas . Data Warehousing Concepts: A thorough understanding of data warehousing concepts and best practices. Programming Languages: Proficiency in Python and PySpark for data manipulation and processing. Data Ingestion & Integration: Experience with data ingestion and data integration processes, ensuring data accuracy and consistency. Data Quality & Governance: Knowledge of data quality and data governance principles and practices. DevOps & CI/CD: Experience with DevOps practices, including CI/CD (Continuous Integration/Continuous Delivery) pipelines, utilizing tools like Azure DevOps and GitHub Actions . Agile Methodology: Familiarity with Agile development methodologies. Performance Tuning & Indexing: Ability to perform performance tuning and indexing to optimize data query performance. Data Validation: Experience in implementing data validation processes to ensure data integrity. Azure Data Lake Storage (ADLS): Experience working with Azure Data Lake Storage (ADLS) . Communication Skills: Excellent communication skills , both written and verbal, to effectively collaborate with stakeholders. Problem-Solving Skills: Strong problem-solving skills to identify and resolve data-related issues. Gemini AI: Familiarity with Gemini AI and its applications in data processing. Experience Required: 6 - 10 years of relevant experience in data engineering and cloud development. This level of seniority requires a proven track record of designing and implementing complex data solutions, leading projects, and mentoring junior team members. What We Offer Salary: 18 LPA

Data Engineer-PowerBi

SACHA Engineering•

Pune

•full time

Job Summary We are seeking an experienced Data Engineer with a specialization in Power BI to join our team in a hybrid work mode. As a Data Engineer - Power BI, you will be responsible for designing, developing, and maintaining large-scale data systems that enable business intelligence and data-driven decision-making. With a strong background in Azure Data Engineering and Power BI, you will play a critical role in shaping our data infrastructure and analytics capabilities. The ideal candidate will have a deep understanding of data engineering principles, expertise in Azure Data Factory, Azure Databricks, and Power BI, and a strong proficiency in DAX. With 7-12 years of experience in the field, you will be able to leverage your technical expertise to drive business growth and improvement through data-driven insights. Key Responsibilities Design, develop, and maintain large-scale data systems using Azure Data Engineering, Azure Data Factory, and Azure Databricks. Develop and maintain complex data models and reports using Power BI. Create and optimize DAX queries to support business intelligence and data analysis. Collaborate with stakeholders to understand business requirements and develop data-driven solutions. Ensure data quality, integrity, and security across the organization. Troubleshoot and resolve data-related issues in a timely and efficient manner. Required Skills & Qualifications The ideal candidate will possess a B Tech degree and have a strong foundation in the following skills: Azure Data Engineering : Experience with designing and implementing data engineering solutions using Azure technologies. Azure Data Factory : Proficiency in using Azure Data Factory for data integration and workflow orchestration. Azure Databricks : Experience with Azure Databricks for big data processing and analytics. Power BI : Strong expertise in developing and maintaining Power BI reports and dashboards. DAX : Proficiency in writing efficient and effective DAX queries for data analysis and visualization. What We Offer A competitive salary range of 2000000 - 2700000 is offered for this role, commensurate with experience.