Senior Data Engineer (Boston)

Senior Data Engineer - Hybrid in Boston

Our client is a global semiconductor leader that bridges the physical and digital worlds to enable breakthroughs. They create solutions that help drive advancements in digitized factories, mobility, and digital healthcare, combat climate change, and reliably connect humans and the world. The team is dedicated to developing technologies that expedite the adoption of electric vehicles and enable rapid transition to 100% carbon-free energy grid.

As a Data Engineer, you will be responsible for designing, developing, and maintaining our data infrastructure, systems, and pipelines. You will collaborate closely with our data scientists, research scientists, and software engineers to ensure the availability, efficiency, and reliability of our data processes and architecture.

Responsibilities

• Implement data security and privacy measures to protect sensitive information.
• Ensure data quality, integrity, and accuracy through data validation, cleansing, and transformation techniques.
• Design, develop, and maintain scalable and efficient data pipelines and ETL (Extract, Transform, Load) processes.
• Identify and implement appropriate data storage and retrieval solutions based on business needs.
• Collaborate with cross-functional teams to gather data requirements and implement data solutions.
• Monitor and optimize data infrastructure performance, troubleshoot issues, and provide timely resolution.
• Continuously evaluate and recommend improvements to our data infrastructure, tools, and processes.

Required Skillset:

• Familiarity with data integration tools (ETL) and frameworks (e.g., Apache Kafka, Apache NiFi, Apache Beam).
• Proven experience as a Data Engineer or similar role, preferably in a fast-paced environment.
• Experience with version control systems (e.g., Git) and collaborative coding practices.
• Master's degree in Computer Science, Engineering, or a related field (or equivalent experience).
• Experience with cloud-based data platforms (e.g., AWS, Azure, GCP) and related services (e.g., S3, Redshift, BigQuery).
• Strong understanding of data modeling, database design, and data warehousing concepts.
• Solid knowledge of SQL and NoSQL databases.
• Proficiency in programming languages such as Python, Java, or Scala.

Desirable Experience

• Knowledge of MLOps workflow tools (e.g., Kubeflow)
• Familiarity with containerization technologies (e.g., Docker, Kubernetes).
• Knowledge of machine learning concepts and frameworks (e.g., TensorFlow, PyTorch).
• PhD in Computer Science, Engineering, or a related field.
• Experience with big data technologies (e.g., Hadoop, Spark) and distributed computing frameworks.