Data Engineer

Guardant Health is a leading precision oncology company focused on helping conquer cancer globally through use of its proprietary tests, vast data sets and advanced analytics. The Guardant Health oncology platform leverages capabilities to drive commercial adoption, improve patient clinical outcomes and lower healthcare costs across all stages of the cancer care continuum. Guardant Health has commercially launched Guardant360 , Guardant360 CDx, Guardant360 TissueNext , Guardant360 Response , and GuardantOMNI tests for advanced stage cancer patients, and Guardant Reveal for early-stage cancer patients. The Guardant Health screening portfolio, including the Shield test, aims to address the needs of individuals eligible for cancer screening.

We are on the hunt for a dynamic and proficient Cloud Data Engineer to join our Guardant Data Platform within the Data Team.

Duties and Responsibilities

Quickly learn and adapt to new technologies as the Data Teams technology stack evolves, demonstrating the ability to tackle new challenges.

Consider all aspects of usability, scalability, deployment, integration, maintenance, and automation when integrating new technology stacks.

Demonstrate strong programming skills in at least one language (Python, Scala, Java) and the ability to learn additional languages as needed.

Build and maintain ETL pipelines and data-driven systems utilizing technologies such as Apache Spark, AWS Glue, Athena, Redshift, and AWS Batch.

Expertise in writing complex SQL queries is essential.

Manage code on GitHub, with a comprehensive understanding of advanced git operations, including git-flow, rebasing, and squashing.

Implement infrastructure as code using Terraform and utilize AWS Analytics and Data Services like Glue, S3, Lambda, AWS Batch, Athena, Redshift, DynamoDB, CloudWatch, Kinesis, SQS, SNS, and DMS.

Use Jenkins to implement deployment pipelines and engage in requirements gathering to estimate efforts for integrating new technology stacks.

Design and architect solutions for ML, Data Governance, Deployment/Integration Automations, and Data Analytics.

Explore and learn additional AWS services such as ECS, ECR, and EC2, along with Data Modeling.

A minimum of 5 years of experience in software development, with at least 2 years focused on building scalable and stable data pipelines using the AWS tech stack.

Proven experience in constructing Data Pipelines in the AWS Cloud, gained through job experience or personal projects. Data and AWS Tools

Strong programming skills and proficiency in SQL.

Familiarity with a range of AWS Analytics Ecosystem components, including but not limited to Apache Airflow, Apache Spark, S3, Glue, Kafka, AWS Athena, Lambda, Redshift, Lake Formation, AWS Batch, ECS - Fargate, Kinesis, Flink, DynamoDB, and SageMaker.

Should have experience in using IaC while deploying the Data Pipelines in AWS. Example - Terraform, CloudFormation, etc.

Experience with Docker, Kubernetes, ECR, EC2, VPC, SNS, SQS, CloudWatch is highly valued.

Experience in building Jenkins deployment pipelines is highly valued.

Proficiency in using collaboration tools like JIRA, Confluence, GitHub is beneficial. Exposure to NoSQL databases is an advantage.