Data Engineer

Data EngineerStrong programming skills in Python or a similar language. Proficiency in SQL and experience with relational and non-relational experience in designing scalable, cost-effective, and maintainable data systems (e.g., data lakes, warehouses, lakehouses).Proven ability to automate and orchestrate complex data pipelines and create resilient, self-healing data systems.Solid understanding of data governance principles and experience implementing data security best practices.Experience translating ambiguous business requirements into precise technical with cloud platforms like AWS, Azure, or Google Cloud.Familiarity with data processing frameworks such as Apache Spark and experience with a data lake environment.Bachelor's degree in Computer Science, Data Science, or a related field.The Data Engineer role will be responsible for implementing and managing the business's data infrastructure to support operations and strategic initiatives. This position plays a crucial part in developing and maintaining a robust, scalable data architecture that ensures data integrity, security, and accessibility across the organization. A typical day involves designing and building ETL/ELT pipelines, collaborating with the Data Architect and business stakeholders, ensuring high data quality, and exploring opportunities for AI and automation. Additionally, this role leverages new technologies to continually enhance the overall data ecosystem and support business growth.Only applicants living in Southern California will be considered.Design, build, and optimize ETL/ELT pipelines to ingest data from various sources into the data ecosystem.Implement processes and tools for data validation, cleaning, and quality assurance to maintain a reliable and accurate data foundation. Work closely with the Data Architect to design and implement a scalable and secure data infrastructure, including data lakes and data warehouses. Collaborate with business stakeholders to understand their data requirements and deliver solutions that enable data-driven decision-making. Utilize container technologies like Docker and Kubernetes to deploy scalable data services and machine learning environments. Implement and enforce data governance and security policies to ensure data privacy and compliance throughout the data lifecycle. Work with business users to assess opportunities and support AI automation with LLMs and agents. Stay current with emerging technologies and best practices in data engineering to continuously improve the data infrastructure.