Senior Data Engineer

• Design, build and optimize the ELT infrastructure supporting business analytics and data science
• Provide a self-service experience wherever possible for internal customers
• Decide when to coach customers in pairing sessions
• Update the documentation or roll up your sleeves and code
• Enable nimble operation of business functions by ensuring low latency, highly reliable and secure delivery of data into the data warehouses
• Design, build and optimize cloud infrastructure and build pipelines for data science projects
• Assist the data science team to independently develop features in a safe and efficient manner
• Contribute to key initiatives that evolve processes, including consolidating the data warehouses or adopting new technologies to support the team

Job Requirements:

• Bachelor s/Master s degree in Engineering, Computer Science (or equivalent experience)
• At least 5+ years of relevant experience managing and processing data in relational databases
• Knowledge of using SQL to extract reports
• 2+ years of experience utilizing Cloud-based Data Warehouses such as Snowflake, Amazon Web
• Services (AWS) RedShift or Google BigQuery
• 1+ years of professional Python programming experience
• Competency in interacting with Application Programming Interfaces (APIs) and Data Processing/Extraction Transform and Load Jobs (ETLs)
• 1+ years of professional experience with the orchestration of ETL workflows, using tools such as Airflow
• 1+ years of professional experience using and administering data analytics tools such as Looker, Tableau, Periscope, or similar
• 1+ years of experience with modern software development practices, including automated build, tests, and deployment (CI/CD, GitOps, etc.)
• Experience in supporting the needs of Data Science (Jupyter Notebook experimentation, Machine Learning (ML) model training, and hosting)
• Familiarity with Infrastructure-as-Code and Configuration-as-Code (Terraform, Pulumi, CloudFormation, etc.)
• Excellent written and verbal English communication skills
• The ability to engage in technical conversation with Analytics users and software engineers
• Demonstrate excellent team and cross-functional collaboration, exhibiting professionalism and
• empathy for other functional groups within the business
• Professional experience with Machine Learning applications, including development and deployment in the cloud
• Competency in disciplines such as DevOps, Cloud Infrastructure, and Microservices
• Expertise in tuning PostgreSQL databases for performance and scalability
• Experience deploying and managing data streaming pipelines based on messaging systems such as AWS SNS/SQS, Kafka, or AWS Kinesis