Senior Data Engineer, Solutions Architecture
About the role
We are seeking a talented Senior Data Engineer to design, build, and maintain our data infrastructure supporting mission-critical energy operations. You'll work at the intersection of renewable energy and data, developing pipelines that process everything from real-time asset performance data to complex trading and risk analytics. This hybrid role offers the opportunity to make a direct impact on clean energy operations while working with a cutting-edge data stack including Snowflake, Dagster, dbt, Modal, and GitLab.
Responsibilities
Design, deploy, and maintain scalable data infrastructure to support enterprise analytics and reporting needs
Manage Snowflake instances, including performance tuning, security configuration, and capacity planning for growing data volumes
Optimize query performance and resource utilization to control costs and improve processing speed
Build and orchestrate complex ETL/ELT workflows using Dagster to ensure reliable, automated data processing for asset management and energy trading
Develop robust data pipelines that handle high-volume, time-sensitive energy market data and asset generation and performance metrics
Implement workflow automation and dependency management for critical business operations
Develop and maintain dbt models to transform raw data into business-ready analytical datasets and dimensional models
Create efficient SQL-based transformations for complex energy market calculations and asset performance metrics
Support advanced analytics initiatives through proper data preparation and feature engineering
Implement comprehensive data validation, testing, and monitoring frameworks to ensure accuracy and consistency across all energy and financial data assets
Establish data lineage tracking and privacy controls to meet regulatory compliance requirements in the energy sector
Develop alerting and monitoring systems for data pipelines, including error handling, SLA monitoring, and incident response
Lead continuous integration and deployment initiatives for Dagster and dbt pipelines, and Streamlit/Gradio application deployments to Linux servers
Implement automated testing and deployment automation for data pipelines and analytics applications
Manage version control and infrastructure as code practices
Partner with Analytics Engineers, Data Scientists, and business stakeholders to understand requirements and deliver solutions
Work closely with asset management and trading groups to ensure real-time data availability for market operations and risk calculations
Collaborate with credit risk teams to develop data models supporting financial analysis and regulatory reporting
Translate business requirements into technical solutions and communicate data insights to stakeholders
Create and maintain technical documentation, data dictionaries, and onboarding materials for data assets
Implement role-based access controls, data encryption, and security best practices across the data stack
Monitor and optimize cloud infrastructure costs, implement resource allocation strategies, and provide cost forecasting
Requirements
Experience: 4+ years of hands-on data engineering experience in production environments
Education: Bachelor's degree in Computer Science, Engineering, or a related field
Data Orchestration: Proficiency in Dagster for pipeline scheduling, dependency management, and workflow automation; Airflow experience a plus
Cloud Data Warehousing: Advanced-level Snowflake administration, including virtual warehouses, clustering, security, and cost optimization
Data Transformation: Proficiency in dbt for data modeling, testing, documentation, and version control of analytical transformations
Programming: Strong Python and SQL skills for data processing and automation
CI/CD: 3+ years of experience with continuous integration and continuous deployment practices and tools; proficiency in GitLab CI/CD required (GitHub Actions experience a plus)
Database Technologies: Advanced SQL skills, database design principles, and experience with multiple database platforms
Cloud Platforms: Proficiency in AWS/Azure/GCP data services, storage solutions (S3, Azure Blob, GCS), and infrastructure as code
Data Integration: Experience with APIs, various data connectors, and formats
Data Security: Understanding of data security best practices, access controls, encryption, and role-based access management
AI & LLM Proficiency: Practical experience integrating and leveraging large language models (e.g., OpenAI, Anthropic, or open-source models) within data workflows; ability to apply LLMs efficiently and securely, with awareness of data privacy boundaries, prompt injection risks, and responsible AI usage in production environments
Problem-Solving: Strong analytical and troubleshooting skills with attention to detail
Communication: Ability to work effectively with both technical and business stakeholders
Work Style: Comfortable with a hybrid work environment (2-3 days in office); highly autonomous and self-directed with a strong work ethic and growth mindset; exceptional attention to detail; able to take ownership of projects end-to-end with minimal oversight
Qualifications
Experience: 4+ years of hands-on data engineering experience in production environments
Education: Bachelor's degree in Computer Science, Engineering, or a related field
Data Orchestration: Proficiency in Dagster for pipeline scheduling, dependency management, and workflow automation; Airflow experience a plus
Cloud Data Warehousing: Advanced-level Snowflake administration, including virtual warehouses, clustering, security, and cost optimization
Database Technologies: Advanced SQL skills, database design principles, and experience with multiple database platforms
Programming: Strong Python and SQL skills for data processing and automation
CI/CD: 3+ years of experience with continuous integration and continuous deployment practices and tools; proficiency in GitLab CI/CD required (GitHub Actions experience a plus)
Cloud Platforms: Proficiency in AWS/Azure/GCP data services, storage solutions (S3, Azure Blob, GCS), and infrastructure as code
Data Integration: Experience with APIs, various data connectors, and formats
Data Security: Understanding of data security best practices, access controls, encryption, and role-based access management
AI & LLM Proficiency: Practical experience integrating and leveraging large language models (e.g., OpenAI, Anthropic, or open-source models) within data workflows; ability to apply LLMs efficiently and securely, with awareness of data privacy boundaries, prompt injection risks, and responsible AI usage in production environments
Problem-Solving: Strong analytical and troubleshooting skills with attention to detail
Communication: Ability to work effectively with both technical and business stakeholders
Work Style: Comfortable with a hybrid work environment (2-3 days in office); highly autonomous and self-directed with a strong work ethic and growth mindset; exceptional attention to detail; able to take ownership of projects end-to-end with minimal oversight
Skills
Data Orchestration: Proficiency in Dagster for pipeline scheduling, dependency management, and workflow automation; Airflow experience a plus
Cloud Data Warehousing: Advanced-level Snowflake administration, including virtual warehouses, clustering, security, and cost optimization
Database Technologies: Advanced SQL skills, database design principles, and experience with multiple database platforms
Programming: Strong Python and SQL skills for data processing and automation
CI/CD: 3+ years of experience with continuous integration and continuous deployment practices and tools; proficiency in GitLab CI/CD required (GitHub Actions experience a plus)
Cloud Platforms: Proficiency in AWS/Azure/GCP data services, storage solutions (S3, Azure Blob, GCS), and infrastructure as code
Data Integration: Experience with APIs, various data connectors, and formats
Data Security: Understanding of data security best practices, access controls, encryption, and role-based access management
AI & LLM Proficiency: Practical experience integrating and leveraging large language models (e.g., OpenAI, Anthropic, or open-source models) within data workflows; ability to apply LLMs efficiently and securely, with awareness of data privacy boundaries, prompt injection risks, and responsible AI usage in production environments
Problem-Solving: Strong analytical and troubleshooting skills with attention to detail
Communication: Ability to work effectively with both technical and business stakeholders
Work Style: Comfortable with a hybrid work environment (2-3 days in office); highly autonomous and self-directed with a strong work ethic and growth mindset; exceptional attention to detail; able to take ownership of projects end-to-end with minimal oversight
Benefits
Clearway offers all eligible employees working 20+ hours per week a comprehensive menu of benefits: generous PTO, medical, dental & vision care, HSAs with company contributions, health FSAs, dependent daycare FSAs, commuter benefits, relocation, & a 401(k) plan with employer match, a variety of life & accident insurances, fertility programs, adoption assistance, generous parental leave, tuition reimbursement, & benefits for employees in same-sex marriages, civil unions & domestic partnerships. For more on Clearway benefits, visit our Benefits Website.
Pay
The pay rate for the successful candidate will depend on geographic location, skills, relevant and demonstrated experience, education, training and certifications, and other factors permitted by law. This role is eligible to earn an annual cash bonus, subject to personal and company performance goals.
Schedule
This role is eligible to work remotely up to 2-3 days per week.