Job Title: Data Engineer (SW) Location: Cupertino, CA (Hybrid Schedule, local consultants only) Duration: 9 Months (Poss. of Extn.) W2 Only Role Keywords: Snowflake, Python, Shell, Unix, SQL, Database, Tableau, GitHub, Spark, Bash Key Qualifications: • 2–5 years of experience in data engineering, software engineering, or data analytics roles. Proficient in SQL and Python; comfortable with Bash or shell scripting. • Hands-on experience with modern data tooling: • Spark for large-scale data processing • Airflow for workflow orchestration, Snowflake and DBT for data transformation and modeling • AWS S3 for data storage and movement • Docker and Kubernetes for containerization and deployment workflows • Jupyter Notebooks for collaborative data exploration and documentation • Familiarity with Git-based CI/CD pipelines and collaborative code development. • Solid understanding of data warehousing, data modeling, and working with big data ecosystems. • Foundational knowledge of statistics, including mean, median, standard deviation, and variance. • Strong problem-solving skills with the ability to break down complex issues into manageable components. • Committed to good software engineering practices such as testing, documentation, and code quality checks. • Able to clearly communicate technical concepts to both technical peers and non-technical stakeholders. • Familiarity with battery systems or electrical engineering is a plus, but not required. Job Description: • As a Data Engineer, you will design, build, and maintain scalable ELT pipelines using SQL and Python. • Work across the full data lifecycle — from ingestion and transformation to model deployment and reporting. • Collaborate with data scientists, engineers, and product managers to deliver clean, reliable, and well-documented data. • Implement and manage workflows using Airflow, while ensuring traceability and version control via GitHub. • Support transformation logic and data modeling using DBT, with data housed primarily in Snowflake. • Use Jupyter Notebooks and ad-hoc analysis to support business questions and drive actionable insights. • Build tools to monitor, validate, and test data pipelines, ensuring high availability and quality. • Contribute to automation efforts, improving the team’s efficiency and reducing manual work. • Provide occasional support for urgent data reporting needs. • Engage constructively with both technical and non-technical colleagues to ensure data solutions align with business goals. Education: • MS or Ph.D. in Computer Science, Software Engineering, Statistics, Electrical Engineering, Battery Engineering, or related technical field. • Certifications in Six Sigma (CSSBB) or Quality Engineering (CQE) are a plus but not required.