Description:

Collective[i] is seeking a Senior Data Engineer with a strong background in AWS DevOps and data engineering to manage and optimize their data infrastructure.
The role involves deploying machine learning models to AWS using SageMaker, requiring expertise in AWS and SageMaker.
Responsibilities include designing, developing, and maintaining ETL pipelines to ensure reliable data flow and high-quality data for analytics and reporting.
The engineer will build and optimize data models in Snowflake, create and maintain complex SQL queries, and conduct orchestration and scheduling through Apache Airflow.
Documentation of data pipelines, architecture, and processes is essential, ensuring clear and updated technical documentation.
The engineer will architect, build, and maintain data science data and models infrastructure on AWS, focusing on scalability, performance, and cost-efficiency.
Collaboration with Data Scientists to deploy machine learning models on AWS SageMaker is a key aspect of the role.
The position also involves automating deployment and monitoring of ML models using CI/CD pipelines and infrastructure-as-code tools like Terraform or AWS CloudFormation.
AWS specific tasks include managing EC2, S3, RDS, VPC, CloudFormation, AutoScaling, CodePipeline, CodeBuild, CodeDeploy, and ECS/EKS.
Setting up and managing monitoring solutions such as CloudWatch to ensure effective operation of data pipelines and deployed models is required.

Requirements:

A Bachelor’s or Master’s degree in Computer Science, Data Engineering, or a related field is required.
Candidates must have 5+ years of experience in Data Engineering, with at least 3+ years working in AWS environments.
Strong knowledge of AWS services, specifically SageMaker, Lambda, Glue, and Redshift, is essential.
Hands-on experience deploying machine learning models in AWS SageMaker is required.
Proficiency in DevOps practices, including CI/CD pipelines, containerization (Docker, ECS, EKS), and infrastructure-as-code tools like Terraform or CloudFormation is necessary.
Advanced SQL skills and experience in building and maintaining complex ETL workflows are required.
Proficiency in Python is essential, with additional skills in Java or Scala being a plus.
Practical experience with Airflow for DAG management and data orchestration is necessary.
Candidates must be proficient in version control (GIT) and containerized deployment with Docker and managed services such as AWS Fargate, ECS, or EKS.
Effective communication and a result-oriented approach are required.

The salary for this position ranges from $100,000 to $170,000 a year, reflecting the diverse and complex nature of the job market.
Collective[i] offers a fully remote work environment, allowing employees to work from wherever they choose.
The company values diversity of experience and backgrounds, fostering a culture of learning and growth alongside a talented team.
Employees have the opportunity to work with cutting-edge technology and contribute to a mission-driven organization focused on helping people and companies prosper.