Principal Data Engineer - AWS

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Full Time Professional Moffett Field, CA, US

8 days ago Requisition

Salary Range: $ To $ Annually

Metis Technology Solutions is seeking an experienced Principal Data Engineer – AWS to join our Software Operations team. This engineer will work closely with software developers, researchers, analysts, database engineers, and system administrators to design, develop, deploy, and operate the data infrastructure and pipelines connecting research platforms, external data sources, cloud-based services, and the project's Sherlock data warehouse.

This position:

• Architects, designs, develops, deploys, and maintains scalable and reliable ETL/ELT data pipelines in Amazon Web Services (AWS).
• Designs automated ingestion and processing solutions for structured, semi-structured, unstructured, batch, and streaming data.
• Develops production-quality data-processing and pipeline software using Python, SQL, and shell scripting.
• Designs and maintain workflow orchestration solutions using technologies such as Apache Airflow, Dagster, AWS Step Functions, or equivalent platforms.
• Designs and implements real-time and near-real-time streaming and event-driven data pipelines using technologies such as AWS Kinesis, Amazon Data Firehose, Kafka, RabbitMQ, SQS/SNS, or equivalent technologies.
• Develops and maintains cloud data solutions using AWS services such as Amazon S3, AWS Lambda, Amazon Redshift, Amazon RDS, Amazon DynamoDB, Amazon EC2, API Gateway, IAM, and Cloud Watch, as appropriate to project requirements.
• Administers and optimizes relational databases and cloud data warehouses, including PostgreSQL/PostGIS and Amazon Redshift or comparable technologies.
• Develops and maintains infrastructure as code (IaC) using Terraform or equivalent technologies.
• Develops and maintains automated deployment and CI/CD processes for data applications and supporting infrastructure.
• Designs pipelines and supporting infrastructure for reliability, scalability, maintainability, observability, security, and efficient use of AWS resources.
• Implements appropriate AWS security practices, including IAM roles and policies, encryption, secrets management, network controls, logging, and least-privilege access.
• Develops monitoring, logging, metrics, and alerting that provide operational visibility into data-pipeline health and performance.
• Documents data architectures, data flows, interfaces, infrastructure, deployment processes, operational procedures, and troubleshooting practices.
• Collaborates with researchers, analysts, and application developers to translate research and application requirements into reliable and maintainable data-processing solutions.

Education:

Bachelor's degree or higher in computer science, computer engineering, information systems, or a related technical discipline.

• Minimum 10 years of progressively responsible software engineering, data engineering, database engineering, or closely related technical experience.
• Minimum 5 years of substantial hands-on AWS experience, including the design, implementation, deployment, and operation of production data-processing or data-pipeline solutions.
• Demonstrated experience architecting and developing ETL/ELT pipelines involving large, heterogeneous, or rapidly changing datasets.
• Strong hands-on experience with AWS data and compute services. Relevant technologies may include S3, Lambda, Redshift, RDS, DynamoDB, Kinesis, Data Firehose, EC2, API Gateway, IAM, and Cloud Watch.
• Advanced SQL skills and substantial experience working with relational database systems, preferably PostgreSQL/PostGIS.
• Demonstrated experience with cloud data warehouses such as Amazon Redshift or comparable technologies.
• Experience with No
  
  SQL databases or document/key-value data stores such as DynamoDB, MongoDB, or comparable technologies.
• Demonstrated experience with data-pipeline and workflow orchestration using Apache Airflow, Dagster, AWS Step Functions, or comparable technologies.
• Experience designing or implementing streaming, message-oriented, or event-driven data-processing systems using technologies such as Kinesis, Data Firehose, Kafka, RabbitMQ, SQS/SNS, or comparable technologies.
• Demonstrated experience diagnosing and correcting database and data-pipeline performance problems.
• Hands-on experience with infrastructure as code,…