.. Signoff, Block level Timing Signoff and ECO generation. Block level Power signoff. Good skill on Automation (Perl/Tcl/Awk/Python) Able to provide technical guidance to Junior Engineer and lead 4-6 engineers. Must have led small project team. Good in communication skill as he/she would be single point .. read more
We are seeking a highly experienced Staff Data Engineer with 10+ years of experience to join our talented team in Bangalore. This position will lead our data engineering efforts in Bangalore. In this strategic role, you will be responsible for designing, implementing, and optimizing complex data solutions using Python, Spark, SQL, Snowflake, dbt, machine learning, and other relevant technologies. Additionally, you will bring deep domain expertise in operations organizations, with a focus on supply chain, product life cycle management, and manufacturing functions. Experience with SAP Hana and Teamcenter applications is a plus. If you're a seasoned data engineer with a proven track record of delivering impactful data solutions in operations contexts, we want to hear from you.
Position Responsibilities: Lead the design, development, and optimization of complex data solutions using Python, Spark, SQL, Snowflake, dbt, machine learning, and other relevant technologies. Apply deep domain expertise in operations organizations, particularly in functions like supply chain, product life cycle management, and manufacturing, to understand data requirements and deliver tailored solutions. Utilize big data processing frameworks such as Apache Spark to process and analyze large volumes of operational data efficiently. Implement advanced data modeling, machine learning algorithms, and predictive analytics to derive actionable insights and drive operational decision-making. Leverage cloud-based data platforms such as Snowflake to store, manage, and analyze structured and semi-structured operational data at scale. Utilize dbt (Data Build Tool) for data modeling, transformation, and documentation to ensure data consistency, quality, and integrity. Monitor and optimize complex data pipelines, ETL processes, and machine learning models for performance, scalability, and reliability in operations contexts. Conduct data profiling, cleansing, and validation to ensure data quality and integrity across different operational data sets. Collaborate closely with cross-functional teams, including operations stakeholders, data scientists, business analysts, and IT teams, to understand operational challenges and deliver actionable insights. Stay updated on emerging technologies, best practices, and industry trends in data engineering, machine learning, and operations management, contributing to continuous improvement and innovation within the organization. All listed requirements are deemed as essential functions to this position; however, business conditions may require reasonable accommodations for additional tasks and responsibilities.
Preferred Experience / Education / Skills: Bachelor's degree in Computer Science, Engineering, Operations Management, or related field. Advanced degree (e.g., Master's or PhD) preferred. 10+ years of experience in data engineering, with proficiency in Python, Spark, SQL, Snowflake, dbt, machine learning, and other relevant technologies. Strong domain expertise in operations organizations, particularly in functions like supply chain, product life cycle management, and manufacturing. Strong domain expertise in life sciences manufacturing equipment, with a deep understanding of industry-specific challenges, processes, and technologies. Experience with big data processing frameworks such as Apache Spark and cloud-based data platforms such as Snowflake. Hands-on experience with data modeling, ETL development, machine learning, and predictive analytics in operations contexts. Familiarity with dbt (Data Build Tool) for managing data transformation and modeling workflows. Excellent problem-solving skills, analytical thinking, and attention to detail. Strong communication and interpersonal skills, with the ability to collaborate effectively with cross-functional teams and operations stakeholders. Experience with SAP, SAP HANA and Teamcenter applications is a plus. Eagerness to learn and adapt to new technologies and tools in a fast-paced environment.
Position Responsibilities: Lead the design, development, and optimization of complex data solutions using Python, Spark, SQL, Snowflake, dbt, machine learning, and other relevant technologies. Apply deep domain expertise in operations organizations, particularly in functions like supply chain, product life cycle management, and manufacturing, to understand data requirements and deliver tailored solutions. Utilize big data processing frameworks such as Apache Spark to process and analyze large volumes of operational data efficiently. Implement advanced data modeling, machine learning algorithms, and predictive analytics to derive actionable insights and drive operational decision-making. Leverage cloud-based data platforms such as Snowflake to store, manage, and analyze structured and semi-structured operational data at scale. Utilize dbt (Data Build Tool) for data modeling, transformation, and documentation to ensure data consistency, quality, and integrity. Monitor and optimize complex data pipelines, ETL processes, and machine learning models for performance, scalability, and reliability in operations contexts. Conduct data profiling, cleansing, and validation to ensure data quality and integrity across different operational data sets. Collaborate closely with cross-functional teams, including operations stakeholders, data scientists, business analysts, and IT teams, to understand operational challenges and deliver actionable insights. Stay updated on emerging technologies, best practices, and industry trends in data engineering, machine learning, and operations management, contributing to continuous improvement and innovation within the organization. All listed requirements are deemed as essential functions to this position; however, business conditions may require reasonable accommodations for additional tasks and responsibilities.
Preferred Experience / Education / Skills: Bachelor's degree in Computer Science, Engineering, Operations Management, or related field. Advanced degree (e.g., Master's or PhD) preferred. 10+ years of experience in data engineering, with proficiency in Python, Spark, SQL, Snowflake, dbt, machine learning, and other relevant technologies. Strong domain expertise in operations organizations, particularly in functions like supply chain, product life cycle management, and manufacturing. Strong domain expertise in life sciences manufacturing equipment, with a deep understanding of industry-specific challenges, processes, and technologies. Experience with big data processing frameworks such as Apache Spark and cloud-based data platforms such as Snowflake. Hands-on experience with data modeling, ETL development, machine learning, and predictive analytics in operations contexts. Familiarity with dbt (Data Build Tool) for managing data transformation and modeling workflows. Excellent problem-solving skills, analytical thinking, and attention to detail. Strong communication and interpersonal skills, with the ability to collaborate effectively with cross-functional teams and operations stakeholders. Experience with SAP, SAP HANA and Teamcenter applications is a plus. Eagerness to learn and adapt to new technologies and tools in a fast-paced environment.