JOB OVERVIEW

We are seeking a skilled Data Engineer to join our dynamic team. The ideal candidate will be responsible for designing, implementing, and maintaining data pipelines and architectures that support our analytics and business intelligence initiatives. You will work closely with data scientists, analysts, and other stakeholders to ensure that data is accessible, reliable, and actionable.

REQUIREMENTS

• Proficiency in programming languages such as Java, Python, and Bash (Unix shell).
• Experience with big data technologies including Hadoop, Apache Hive, Spark, and Informatica.
• Familiarity with cloud platforms like AWS or Azure Data Lake.
• Strong knowledge of database design principles with experience in Microsoft SQL Server, Oracle, or similar systems.
• Understanding of linked data concepts and analytics tools like Looker.
• Experience working in Agile development environments.
• Excellent analysis skills with the ability to troubleshoot complex issues effectively.
• Knowledge of Talend or similar ETL tools is a plus.
• Familiarity with shell scripting for automation tasks is desirable.
• Strong communication skills to collaborate effectively with technical and non-technical stakeholders. Join us in leveraging the power of data to drive strategic decisions within the organization. If you are passionate about building scalable data solutions and thrive in a collaborative environment, we encourage you to apply.
  Location: San Francisco, CA (Hybrid)
  Job Types: Full-time, Contract
  Pay: $140,000.00 - $168,083.45 per year
  Work Location: On the roa

• Design and develop robust data pipelines using ETL processes to facilitate data integration from various sources.
• Collaborate with cross-functional teams to gather requirements and translate them into technical specifications.
• Implement and manage big data technologies such as Hadoop, Spark, and Azure Data Lake to handle large datasets efficiently.
• Utilize SQL for database querying and management in Microsoft SQL Server and Oracle environments.
• Develop data models and maintain data warehouses to support analytical needs.
• Create RESTful APIs for seamless data access across applications.
• Conduct model training and implement machine learning algorithms as necessary.
• Ensure data quality through rigorous testing and validation processes.
• Document processes, designs, and workflows for future reference and compliance.
• Stay updated on industry trends and best practices in data engineering.