Core Natural Resources, Inc.
Cloud & Data Engineer - On-site Only
Core Natural Resources, Inc., Canonsburg, Pennsylvania, United States
Job Description
CONSOL Energy, 275 Technology Drive Suite 101, Canonsburg, Pennsylvania, United States of America
Posted Monday, August 25, 2025 at 4:00 AM
The Cloud & Data Engineer supports the design, build, and maintenance of Core’s cloud infrastructure, data lake environment, and backend service integrations. Operating under the Technology Strategist & Cloud Engineering Manager, this role delivers production-grade systems that enable analytics, automation, and reliable operations across enterprise platforms.
Key Responsibilities
Accept, embrace, and promote the following Core Values of Core Natural Resources: Safety, Sustainability, Continuous Improvement
Implement and maintain cloud infrastructure using infrastructure-as-code and CI/CD pipelines (Terraform, Git-based workflows)
Develop, test, and monitor ETL/ELT data pipelines for ingestion, transformation, and analytics-ready data flows
Maintain and scale Core’s cloud-native data lake to support business reporting, data quality, and financial operations
Build, manage, and operationalize backend APIs and service integrations, ensuring secure and stable data exchange
Support internal applications and operational tooling deployments — focusing on automation, availability, and performance
Implement observability tools (e.g., logging, tracing, monitoring, alerts) across infra and data systems
Participate in incident resolution, root-cause analysis, and ongoing ops improvement
Create and maintain technical documentation: pipeline architecture, API contracts, infrastructure diagrams, and data lineage
Collaborate with internal teams to deliver consistent and auditable cloud solutions aligned to enterprise standards
Required Qualifications
Bachelor’s degree in Computer Science, Software Engineering, Data Engineering, or a related technical discipline
Professional experience in cloud or data engineering roles, with demonstrated ownership of production-grade deliverables
AWS Certified Solutions Architect Associate (or higher) required. Applicants without AWS certification must complete Certified Solutions Architect Associate certification within 60 days of hire
Must have experience working in environments with multi-account AWS architectures, enterprise security controls, and cost optimization practices.
Ability to independently deliver end-to-end cloud/data solutions from architecture to production with minimal oversight
Demonstrated ability to integrate AWS data pipelines with at least one ERP or enterprise financial platform in production
Hands-on experience with AWS services including S3, Lambda, RDS, IAM, and ETL tools such as Glue and Step Functions
Proficiency in Python and SQL for data processing, transformation, and scripting
Demonstrated experience developing, integrating, or consuming RESTful APIs in a backend context
Proven experience deploying infrastructure using Terraform in a team-based GitOps workflow
Solid understanding of data lake architecture, data modeling principles, and pipeline orchestration (batch and streaming)
Experience with monitoring and observability tooling (e.g., CloudWatch, Prometheus, Grafana), including alerting and dashboarding
Preferred Qualifications
1–3 years of professional experience in production-grade cloud, data, or backend engineering roles.
Hands-on experience with orchestration platforms such as Apache Airflow, dbt, or AWS-native equivalents for automated pipeline management.
Proficiency in containerization and backend service deployment workflows, including Docker and orchestration on ECS or EKS, with knowledge of CI/CD integration.
Experience developing and maintaining backend applications using Python frameworks such as Django or FastAPI, or Node.js frameworks such as Express.js.
Familiarity with microservices architecture and service-to-service communication using REST or gRPC.
Understanding of event-driven architectures using AWS SNS, SQS, Kinesis, or Kafka.
Experience with unit testing, integration testing, and continuous testing practices (e.g., PyTest, Jest).
Knowledge of relational and NoSQL databases (e.g., PostgreSQL, DynamoDB, MongoDB) and ORM frameworks (e.g., Django ORM, SQLAlchemy).
Experience deploying and integrating machine learning models into production systems using frameworks such as scikit-learn, TensorFlow, or PyTorch.
Familiarity with MLOps workflows — model packaging, versioning (e.g., MLflow), and monitoring in a cloud environment.
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Posted Monday, August 25, 2025 at 4:00 AM
The Cloud & Data Engineer supports the design, build, and maintenance of Core’s cloud infrastructure, data lake environment, and backend service integrations. Operating under the Technology Strategist & Cloud Engineering Manager, this role delivers production-grade systems that enable analytics, automation, and reliable operations across enterprise platforms.
Key Responsibilities
Accept, embrace, and promote the following Core Values of Core Natural Resources: Safety, Sustainability, Continuous Improvement
Implement and maintain cloud infrastructure using infrastructure-as-code and CI/CD pipelines (Terraform, Git-based workflows)
Develop, test, and monitor ETL/ELT data pipelines for ingestion, transformation, and analytics-ready data flows
Maintain and scale Core’s cloud-native data lake to support business reporting, data quality, and financial operations
Build, manage, and operationalize backend APIs and service integrations, ensuring secure and stable data exchange
Support internal applications and operational tooling deployments — focusing on automation, availability, and performance
Implement observability tools (e.g., logging, tracing, monitoring, alerts) across infra and data systems
Participate in incident resolution, root-cause analysis, and ongoing ops improvement
Create and maintain technical documentation: pipeline architecture, API contracts, infrastructure diagrams, and data lineage
Collaborate with internal teams to deliver consistent and auditable cloud solutions aligned to enterprise standards
Required Qualifications
Bachelor’s degree in Computer Science, Software Engineering, Data Engineering, or a related technical discipline
Professional experience in cloud or data engineering roles, with demonstrated ownership of production-grade deliverables
AWS Certified Solutions Architect Associate (or higher) required. Applicants without AWS certification must complete Certified Solutions Architect Associate certification within 60 days of hire
Must have experience working in environments with multi-account AWS architectures, enterprise security controls, and cost optimization practices.
Ability to independently deliver end-to-end cloud/data solutions from architecture to production with minimal oversight
Demonstrated ability to integrate AWS data pipelines with at least one ERP or enterprise financial platform in production
Hands-on experience with AWS services including S3, Lambda, RDS, IAM, and ETL tools such as Glue and Step Functions
Proficiency in Python and SQL for data processing, transformation, and scripting
Demonstrated experience developing, integrating, or consuming RESTful APIs in a backend context
Proven experience deploying infrastructure using Terraform in a team-based GitOps workflow
Solid understanding of data lake architecture, data modeling principles, and pipeline orchestration (batch and streaming)
Experience with monitoring and observability tooling (e.g., CloudWatch, Prometheus, Grafana), including alerting and dashboarding
Preferred Qualifications
1–3 years of professional experience in production-grade cloud, data, or backend engineering roles.
Hands-on experience with orchestration platforms such as Apache Airflow, dbt, or AWS-native equivalents for automated pipeline management.
Proficiency in containerization and backend service deployment workflows, including Docker and orchestration on ECS or EKS, with knowledge of CI/CD integration.
Experience developing and maintaining backend applications using Python frameworks such as Django or FastAPI, or Node.js frameworks such as Express.js.
Familiarity with microservices architecture and service-to-service communication using REST or gRPC.
Understanding of event-driven architectures using AWS SNS, SQS, Kinesis, or Kafka.
Experience with unit testing, integration testing, and continuous testing practices (e.g., PyTest, Jest).
Knowledge of relational and NoSQL databases (e.g., PostgreSQL, DynamoDB, MongoDB) and ORM frameworks (e.g., Django ORM, SQLAlchemy).
Experience deploying and integrating machine learning models into production systems using frameworks such as scikit-learn, TensorFlow, or PyTorch.
Familiarity with MLOps workflows — model packaging, versioning (e.g., MLflow), and monitoring in a cloud environment.
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