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Data Engineer
Skills
Computer ScienceData EngineeringAirflowApache KafkaData ModelsData ScienceDatasets
What the job involves
The main requirements, responsibilities and hiring steps.
Requirements
- BS or MS in a technical field such as computer science or engineering
- 5+ years of technical experience working with data
- 5+ years building scalable data services and applications using SQL Python Java or Kotlin with an interest in learning additional tools and technologies
- Deep understanding of microservices architecture and RESTful API development including gRPC REST/SOAP and GraphQL
- Experience with AWS services including SQS SNS and familiarity with real-time data processing frameworks like Apache Kafka or AWS Kinesis
- Significant experience building and deploying data-related infrastructure robust data pipelines and ETL/ELT code encompassing messaging storage compute and transformation execution
- Experience identifying and proposing initiatives to enhance the performance and efficiency of existing systems
- Strong communication and interpersonal skills
- Experience managing a team or working with an on-shore/off-shore model
- Fluent in English
- Independent engineer working ~40 hours/week Monday-Friday and being available 9am-12pm PT
- Availability until noon Pacific time zone answering in Slack and participating in occasional meetings
- Camera and microphone required during the interview and for team meetings/discussions
Nice to have
- Knowledge of AWS and Azure cloud services
- Previous experience in a start-up or agile environment
- Experience with Snowflake and Airflow
Day to day
- Improve and maintain the data services platform
- Deliver high-quality data services promptly ensuring data governance and integrity while meeting objectives and maintaining SLAs for data sharing across multiple products
- Develop effective architectures and produce key code components that contribute to the design implementation and maintenance of technical solutions
- Integrate a diverse network of third-party tools into a cohesive scalable platform
- Continuously improve system performance and reliability by diagnosing and resolving unexpected operational issues to prevent recurrence
- Ensure that your team’s work undergoes rigorous testing through repeatable automated methods
- Support data infrastructure and the rest of the data team who design implement and deploy scalable fault-tolerant pipelines that ingest and refine large diverse datasets into simplified accessible data models in production
- Collaborate with cross-functional teams to understand data flows and design build and test optimal solutions for engineering challenges
- Operate within an Agile/Scrum framework working closely with Product and Engineering teams to deliver value across multiple services and products
- Influence and shape the enterprise data platform and services roadmap architecture and design standards
- Collaborate with technology leaders and team members to design adapt and enhance the architecture to meet evolving business needs
Hiring process
- Interview
- Background investigation