Senior Data Engineer, Data Platforms and Integrations

Global Partners LP•Newton, MA

3d•$182,300 - $190,000

About The Position

Global Partners is hiring a Senior Data Engineer for its Data Platforms and Integrations work. The role architects, builds, and operates production data ingestion, integration, analytics, and data-platform solutions. It designs scalable AWS pipelines with Dagster; develops tested analytics models and transformations in Snowflake and dbt; and deploys containerized platform services on AWS ECS/Fargate with Docker and CloudFormation. The position establishes CI/CD workflows with GitHub Actions, manages secure configurations and credentials through AWS Secrets Manager, and implements monitoring, alerting, observability, data-quality controls, and privacy safeguards across development, test, and production environments. The role builds and supports source-ingestion, API, reverse-ETL, and backend data applications using tools including Fivetran, Python, FastAPI, and Streamlit. It develops master data management solutions, governed single sources of truth, and automated data-governance guardrails for key business entities. The engineer applies AI-assisted development tools, contributes reusable AI development capabilities, and builds MCP servers and other scalable data products using modern product-development and agile practices. The Senior Data Engineer partners with terminal, wholesale, retail, supply, and trading teams; provides technical leadership and mentorship; maintains architecture documentation and operational runbooks; and champions automation-first engineering standards. The role requires a master’s degree in Computer Science, Information Technology, or a related field, and five years of experience in a related role. The stated experience may be gained concurrently. The pay range is $182,300–$190,000; the final starting amount depends on factors including experience, skills, abilities, and geographic location.

Requirements

Master’s degree in Computer Science, Information Technology, or a related field.
Five years of experience as a Data Engineer, Software Developer, Systems Analyst/Architect, or in a related role.
Five years of experience designing, developing, and supporting integration solutions.
Five years of experience programming in Python on data-intensive applications.
Five years of experience with REST API protocols and Python API frameworks such as FastAPI.
Five years of experience working with SQL and NoSQL databases.
Four years of experience building cloud-native data solutions on AWS or Azure.
Four years of experience working with Docker and Kubernetes.
Two years of experience building data pipelines with Fivetran, Dagster, dbt, and Snowflake.
Two years of experience implementing CI/CD using GitHub Actions.
Two years of experience authoring infrastructure-as-code using AWS CloudFormation or Terraform.
Two years of experience implementing data-pipeline observability and monitoring using tools such as Prometheus, Grafana, or Metaplane.
Two years of experience building and maintaining data pipelines that comply with privacy and security regulations such as HIPAA/GDPR.
Experience requirements may be gained concurrently.

Responsibilities

Architect, build, and operate production data ingestion, integration, analytics, and data-platform solutions.
Design and orchestrate scalable data pipelines in AWS using Dagster.
Develop tested analytics models and transformation processes in Snowflake and dbt.
Deploy containerized platform services on AWS ECS/Fargate using Docker and CloudFormation.
Establish CI/CD workflows through GitHub Actions.
Manage secure configurations and credentials through AWS Secrets Manager.
Implement monitoring, alerting, observability, data-quality controls, and privacy safeguards across development, test, and production environments.
Build and support source-ingestion, API, reverse-ETL, and backend data applications using tools such as Fivetran, Python, FastAPI, and Streamlit.
Develop master data management solutions, governed single sources of truth, and automated data-governance guardrails for key business entities.
Apply AI-assisted development tools, contribute reusable AI development capabilities, and build MCP servers and other scalable data products.
Partner with terminal, wholesale, retail, supply, and trading teams.
Provide technical leadership and mentorship.
Maintain architecture documentation and operational runbooks.
Champion automation-first engineering standards.