Data Engineer

Open to both Austin, Texas and Cupertino, California locations! Description: • We are seeking experienced Data Engineers to join a high-impact team focused on building and optimizing modern data platforms. This role is ideal for hands-on engineers who thrive in fast-paced environments and can independently design, develop, and maintain scalable data solutions. • You will play a critical role in enabling data-driven decision-making by developing robust pipelines, ensuring data quality, and supporting advanced analytics and machine learning initiatives. This is a highly technical position requiring strong coding ability, problem-solving skills, and real-world experience with large-scale data systems. Qualifications: • 6+ years of hands-on data engineering experience • Strong programming skills in Python and SQL (Scala is highly preferred) • Spark / PySpark • Kafka • Airflow or similar orchestration tools • Docker & Kubernetes (required) • Spark architecture and performance tuning • Data warehousing concepts and best practices • Pipeline design and optimization • Experience with cloud platforms (AWS, Azure, or GCP) • Ability to troubleshoot complex data and system issues • Strong communication skills and ability to work independently Preferred Qualifications: • Experience with data modeling and lakehouse architectures • Familiarity with CI/CD, data observability, and infrastructure-as-code • Exposure to ML pipelines or advanced analytics workflows • Experience with tools such as Snowflake, Databricks, or similar platforms Responsibilities: • Design and build scalable batch and near real-time data pipelines • Develop and optimize ETL/ELT workflows for performance and cost efficiency • Work with Spark-based systems, including understanding job execution and optimization • Design and implement data models (e.g., star schema, medallion architecture) • Support machine learning and advanced data use cases (feature engineering, retraining workflows, etc.) • Ensure data quality, governance, privacy, and system reliability • Troubleshoot production issues and handle real-world incident management scenarios • Collaborate on data pipeline orchestration and job scheduling • Continuously improve system performance and scalability