Sr Data Engineer

Responsibilities:

• Lead the design, development, and optimization of data pipelines, ETL processes, and data integration solutions using Python, Spark, SQL, Snowflake, dbt, and other relevant technologies.
• Apply strong domain expertise in operations organizations, particularly in functions like supply chain 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 data transformations, aggregations, and business logic to support analytics, reporting, and operational decision-making.
• Leverage cloud-based data platforms such as Snowflake to store and manage 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 data pipelines and ETL processes 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, and business analysts, to understand operational challenges and deliver actionable insights.
• Stay updated on emerging technologies and best practices in data engineering and operations management, contributing to continuous improvement and innovation within the organization.

Preferred Experience/Education/Skills:

• Bachelors degree in Computer Science, Engineering, Operations Management, or related field.
• 5+ years of experience in data engineering, with proficiency in Python, Spark, SQL, Snowflake, dbt, and other relevant technologies.
• Strong domain expertise in operations organizations, particularly in functions like supply chain 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, and data integration in operations contexts.
• Familiarity with dbt (Data Build Tool) for managing data transformation and modeling workflows.
• Familiarity with reporting and visualization tools like Tableau, Powerbi etc.
• Good understanding of advanced data engineering and data science practices and technologies like pypark, sagemaker, cloudera MLflow etc.
• Experience with SAP, SAP HANA and Teamcenter applications is a plus.
• 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.
• Eagerness to learn and adapt to new technologies and tools in a fast-paced environment.