Strada Education Network
Data Operations & Cloud Engineer
Strada Education Network, Washington, District of Columbia, us, 20022
The
Data Operations & Cloud Engineer
will oversee the development, management, and optimizaiton of a cloud data platform on
Google Cloud Platform (GCP)
. This role will ensure secure, efficient, and scalable data solutions and be instrumental in establishing and maintaining the data infrastructure that supports advanced analytics and research on workforce development, higher education, labor market, and economic data for state partners. Key responsibilities include managing data ingestion pipelines, optimizing system performance, implementing data governance policies, and providing technical leadership to support data-driven decision making. Our data environment leverages GCP services such as
BigQuery, Cloud Run, Cloud Storage, and Vertex AI
. We welcome candidates with experience in comparable cloud data ecosystems (e.g. Snowflake, Amazon Redshift, Databricks) who are eager to pivot their expertise to GCP's toolset in support of our mission. Context Strada Education Foundation supports programs, policies, and organizations that strengthen connections between education and employment in the U.S., with a special focus on helping those who have faced the greatest challenges in securing economic opportunity through postsecondary education and training. Strada’s strategic plan focuses on five key areas to improve pathways to opportunity in post-high school education: Clear outcomes
– defining and measuring educational outcomes Quality coaching
– providing guidance to learners Affordability
– ensuring education and training is financially attainable Work-based learning
– expanding on-the-job training opportunities Employer alignment
– aligning educational programs with workforce needs Strada leverages research, strategic philanthropy, investments, communications, advocacy, and collaboration in the pursuit of this mission. The Data Operations Engineer supports the
Education Analytics and Technical Services
team within the Employer Alignment focus area, ensuring that data infrastructure and practices meet the needs of these strategic initiatives. Key responsibilities:
the Data Operations Engineer has
four core responsibility areas
, listed below with the approximate percentage of time required for execution. Data Management (40%)
Cloud Data Platform Administration: Manage, maintain, and optimize the Google Cloud-based data warehouse and storage environment (e.g. BigQuery , Cloud Storage ) to ensure secure, efficient, and scalable data solutions. ETL/ELT Pipeline Development: Develop and orchestrate scalable ETL/ELT data pipelines for data ingestion and transformation, using GCP services (such as Cloud Run or Cloud Dataflow) to handle large-scale data processing. Data Quality & Governance: Ensure data integrity, quality, and governance compliance across all workflows, establishing best practices for data security, access control, and regulatory compliance. Third-Party Data Integration: Collaborate with internal research teams and state agency partners to integrate third-party data sources (e.g. via APIs and data marketplaces) into the platform. Identify and onboard new data sources and technology to expand the workforce and education data model. Performance Optimization: Optimize data structures, partitioning, and query strategies for performance and cost efficiency in BigQuery. Monitor and tune resource usage to ensure cost-effective operations. System Administration and Performance Optimization (30%)
Monitoring & Troubleshooting: Monitor system performance and troubleshoot issues across GCP data services. Ensure high availability and responsiveness of databases, pipelines, and applications. Resource & Cost Management: Manage storage resources, query performance, and workload scheduling in the GCP environment (BigQuery, Cloud Run, etc.). Implement cost-effective strategies for managing cloud data warehouse expenditures, including rightsizing storage and compute resources. Automation & DevOps: Automate data workflows, pipeline scheduling, and deployments (leveraging Infrastructure-as-Code and CI/CD where possible) to streamline data processing and reporting. Maintain comprehensive documentation for data models, system configurations, and integration processes to support maintainability and knowledge sharing. Data Collaboration and Reporting Support (20%)
Analytics Enablement: Provide technical data access and support to the Education Analytics and Technical Services team working within the data platform. Ensure that analysts and researchers can easily retrieve and analyze data needed for workforce and education insights. Data Quality Collaboration: Drive data quality improvements through close collaboration with stakeholders and contractors, supporting effective analytics and reporting outcomes. Establish feedback loops to continually refine data definitions and accuracy. Dashboard and App Support: Assist in the development and maintenance of analytics dashboards and applications by ensuring data is accessible, well-structured, and up to date. This includes supportingbusiness intelligence toolslikeTableauand custom analytics apps (e.g.Streamlit) by provisioning data and optimizing queries for front-end use. Alignment with Analytics Needs: Ensure ongoing alignment between the data infrastructure and the analytic capabilities of the team. Work closely with analysts to understand their data needs and adjust data models or pipelines to enable new metrics, visualizations, and insights. Team Leadership & DEI Commitment (10%)
Technical Leadership: Provide guidance and training to research and analytics team members on best practices in data warehousing, data engineering, and governance. Foster data literacy and efficient use of the data platform across the team. Diversity, Equity & Inclusion: Partner with Human Resources and DEI leadership to promote equitable workplace practices and embrace diverse perspectives in data operations. Collaborative Culture: Foster a collaborative, inclusive environment that encourages innovation and cross-functional teamwork. Model transparency, respect, and inclusion in all professional interactions, ensuring that all team members feel valued and heard as we develop data solutions. The Person: Qualifications and Experience
Education: Bachelor’s degree in computer science, data engineering, information systems, or a related field (or equivalent work experience). A master’s degree is a plus. Cloud Data Platform Expertise: 5+ years of experience in enterprise environments with multiple internal and external stakeholders managing and operating cloud-based data platforms, preferably onGoogle Cloud Platform(BigQuery, Cloud Storage, etc.). Experience withother data warehouse ecosystemssuch as Snowflake, Amazon Redshift, or Databricks is highly valued, with an expectation of willingness to pivot and learn GCP tools. Data Modeling & Architecture: Proven experience in designing and implementing data models that facilitate continuous research, dashboarding, reporting, and advanced analytics. Ability to optimize data workflows and performance for large-scale datasets. Programming & Scripting: Strong proficiency in SQL for data manipulation and query optimization. Experience with Python (or similar languages) for data engineering tasks and script automation (e.g., using Pandas, Apache Beam/Dataflow). ETL/ELT & Integration: Hands-on experience with ETL/ELT pipeline development and cloud-based data integration processes.Experience integrating third-party datafrom external APIs and data marketplaces to enrich internal datasets. CI/CD:
Familiarity with tools such as Terraform, Cloud Build, GitHub Actions, or Jenkins for infrastructure provisioning and deployment automation. Data Governance & Security:
Demonstrated expertise managing security, permissions, and controls for a large organization.Knowledge of key U.S. data privacy regulations (e.g., FERPA, CCPA,) and cloud compliance frameworks (e.g., SOC 2, ISO 27001) for data handling in education and labor contexts is essential. Version Control: Proficient with Git for version control and collaborative development. Analytical Tools: Familiarity with business intelligence and data visualization tools such asTableau(preferred) or Power BI, and exposure to building simple analytics applications or dashboards to support end-users. Domain Experience: Experience working withstate-level workforce, education, and/or economic datasetsis a strong plus. An understanding of labor market data or higher education data conventions will help in contextualizing and validating data. Soft Skills: Excellent problem-solving and troubleshooting skills in a data-centric environment. Strong communication and collaboration abilities, with experience working in cross-functional teams and explaining technical concepts to non-technical stakeholders. Experience with agile project management methodologies for data infrastructure projects is beneficial. Desired Certifications: Current Google Cloud Professional Data Engineer and/or Cloud Architect certification. $115,300 - $137,600 a year plus annual bonus. The pay range listed is based on national compensation benchmark data and may vary depending on skills, experience, job-related knowledge, variations in cost of labor, and in some cases, geographic location. The exact job offer will be determined based on several factors such as the candidate’s individual skills, qualifications and experience relative to the requirements of the role. The range displayed with the job posting represents the minimum and maximum target for new hire salaries for the position across the U.S.The company also reviews and considers internal equity (current employee salary) when hiring new employees to the organization. The range is the expected starting base salary for someone hired into this position with room to grow professionally, including increased earning potential beyond the starting pay range. Beyond a new hire’s base salary, Strada also offers all full-time employees a comprehensive employee benefit package.
Travel Requirements: This role may require occasional travel for conferences, meetings, or site visits, estimated at up to 10% of the time. Please note:
We are unable to offer visa sponsorship for this position. Applicants must be legally authorized to work in the United States on a full-time basis without current or future sponsorship requirements.
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Data Operations & Cloud Engineer
will oversee the development, management, and optimizaiton of a cloud data platform on
Google Cloud Platform (GCP)
. This role will ensure secure, efficient, and scalable data solutions and be instrumental in establishing and maintaining the data infrastructure that supports advanced analytics and research on workforce development, higher education, labor market, and economic data for state partners. Key responsibilities include managing data ingestion pipelines, optimizing system performance, implementing data governance policies, and providing technical leadership to support data-driven decision making. Our data environment leverages GCP services such as
BigQuery, Cloud Run, Cloud Storage, and Vertex AI
. We welcome candidates with experience in comparable cloud data ecosystems (e.g. Snowflake, Amazon Redshift, Databricks) who are eager to pivot their expertise to GCP's toolset in support of our mission. Context Strada Education Foundation supports programs, policies, and organizations that strengthen connections between education and employment in the U.S., with a special focus on helping those who have faced the greatest challenges in securing economic opportunity through postsecondary education and training. Strada’s strategic plan focuses on five key areas to improve pathways to opportunity in post-high school education: Clear outcomes
– defining and measuring educational outcomes Quality coaching
– providing guidance to learners Affordability
– ensuring education and training is financially attainable Work-based learning
– expanding on-the-job training opportunities Employer alignment
– aligning educational programs with workforce needs Strada leverages research, strategic philanthropy, investments, communications, advocacy, and collaboration in the pursuit of this mission. The Data Operations Engineer supports the
Education Analytics and Technical Services
team within the Employer Alignment focus area, ensuring that data infrastructure and practices meet the needs of these strategic initiatives. Key responsibilities:
the Data Operations Engineer has
four core responsibility areas
, listed below with the approximate percentage of time required for execution. Data Management (40%)
Cloud Data Platform Administration: Manage, maintain, and optimize the Google Cloud-based data warehouse and storage environment (e.g. BigQuery , Cloud Storage ) to ensure secure, efficient, and scalable data solutions. ETL/ELT Pipeline Development: Develop and orchestrate scalable ETL/ELT data pipelines for data ingestion and transformation, using GCP services (such as Cloud Run or Cloud Dataflow) to handle large-scale data processing. Data Quality & Governance: Ensure data integrity, quality, and governance compliance across all workflows, establishing best practices for data security, access control, and regulatory compliance. Third-Party Data Integration: Collaborate with internal research teams and state agency partners to integrate third-party data sources (e.g. via APIs and data marketplaces) into the platform. Identify and onboard new data sources and technology to expand the workforce and education data model. Performance Optimization: Optimize data structures, partitioning, and query strategies for performance and cost efficiency in BigQuery. Monitor and tune resource usage to ensure cost-effective operations. System Administration and Performance Optimization (30%)
Monitoring & Troubleshooting: Monitor system performance and troubleshoot issues across GCP data services. Ensure high availability and responsiveness of databases, pipelines, and applications. Resource & Cost Management: Manage storage resources, query performance, and workload scheduling in the GCP environment (BigQuery, Cloud Run, etc.). Implement cost-effective strategies for managing cloud data warehouse expenditures, including rightsizing storage and compute resources. Automation & DevOps: Automate data workflows, pipeline scheduling, and deployments (leveraging Infrastructure-as-Code and CI/CD where possible) to streamline data processing and reporting. Maintain comprehensive documentation for data models, system configurations, and integration processes to support maintainability and knowledge sharing. Data Collaboration and Reporting Support (20%)
Analytics Enablement: Provide technical data access and support to the Education Analytics and Technical Services team working within the data platform. Ensure that analysts and researchers can easily retrieve and analyze data needed for workforce and education insights. Data Quality Collaboration: Drive data quality improvements through close collaboration with stakeholders and contractors, supporting effective analytics and reporting outcomes. Establish feedback loops to continually refine data definitions and accuracy. Dashboard and App Support: Assist in the development and maintenance of analytics dashboards and applications by ensuring data is accessible, well-structured, and up to date. This includes supportingbusiness intelligence toolslikeTableauand custom analytics apps (e.g.Streamlit) by provisioning data and optimizing queries for front-end use. Alignment with Analytics Needs: Ensure ongoing alignment between the data infrastructure and the analytic capabilities of the team. Work closely with analysts to understand their data needs and adjust data models or pipelines to enable new metrics, visualizations, and insights. Team Leadership & DEI Commitment (10%)
Technical Leadership: Provide guidance and training to research and analytics team members on best practices in data warehousing, data engineering, and governance. Foster data literacy and efficient use of the data platform across the team. Diversity, Equity & Inclusion: Partner with Human Resources and DEI leadership to promote equitable workplace practices and embrace diverse perspectives in data operations. Collaborative Culture: Foster a collaborative, inclusive environment that encourages innovation and cross-functional teamwork. Model transparency, respect, and inclusion in all professional interactions, ensuring that all team members feel valued and heard as we develop data solutions. The Person: Qualifications and Experience
Education: Bachelor’s degree in computer science, data engineering, information systems, or a related field (or equivalent work experience). A master’s degree is a plus. Cloud Data Platform Expertise: 5+ years of experience in enterprise environments with multiple internal and external stakeholders managing and operating cloud-based data platforms, preferably onGoogle Cloud Platform(BigQuery, Cloud Storage, etc.). Experience withother data warehouse ecosystemssuch as Snowflake, Amazon Redshift, or Databricks is highly valued, with an expectation of willingness to pivot and learn GCP tools. Data Modeling & Architecture: Proven experience in designing and implementing data models that facilitate continuous research, dashboarding, reporting, and advanced analytics. Ability to optimize data workflows and performance for large-scale datasets. Programming & Scripting: Strong proficiency in SQL for data manipulation and query optimization. Experience with Python (or similar languages) for data engineering tasks and script automation (e.g., using Pandas, Apache Beam/Dataflow). ETL/ELT & Integration: Hands-on experience with ETL/ELT pipeline development and cloud-based data integration processes.Experience integrating third-party datafrom external APIs and data marketplaces to enrich internal datasets. CI/CD:
Familiarity with tools such as Terraform, Cloud Build, GitHub Actions, or Jenkins for infrastructure provisioning and deployment automation. Data Governance & Security:
Demonstrated expertise managing security, permissions, and controls for a large organization.Knowledge of key U.S. data privacy regulations (e.g., FERPA, CCPA,) and cloud compliance frameworks (e.g., SOC 2, ISO 27001) for data handling in education and labor contexts is essential. Version Control: Proficient with Git for version control and collaborative development. Analytical Tools: Familiarity with business intelligence and data visualization tools such asTableau(preferred) or Power BI, and exposure to building simple analytics applications or dashboards to support end-users. Domain Experience: Experience working withstate-level workforce, education, and/or economic datasetsis a strong plus. An understanding of labor market data or higher education data conventions will help in contextualizing and validating data. Soft Skills: Excellent problem-solving and troubleshooting skills in a data-centric environment. Strong communication and collaboration abilities, with experience working in cross-functional teams and explaining technical concepts to non-technical stakeholders. Experience with agile project management methodologies for data infrastructure projects is beneficial. Desired Certifications: Current Google Cloud Professional Data Engineer and/or Cloud Architect certification. $115,300 - $137,600 a year plus annual bonus. The pay range listed is based on national compensation benchmark data and may vary depending on skills, experience, job-related knowledge, variations in cost of labor, and in some cases, geographic location. The exact job offer will be determined based on several factors such as the candidate’s individual skills, qualifications and experience relative to the requirements of the role. The range displayed with the job posting represents the minimum and maximum target for new hire salaries for the position across the U.S.The company also reviews and considers internal equity (current employee salary) when hiring new employees to the organization. The range is the expected starting base salary for someone hired into this position with room to grow professionally, including increased earning potential beyond the starting pay range. Beyond a new hire’s base salary, Strada also offers all full-time employees a comprehensive employee benefit package.
Travel Requirements: This role may require occasional travel for conferences, meetings, or site visits, estimated at up to 10% of the time. Please note:
We are unable to offer visa sponsorship for this position. Applicants must be legally authorized to work in the United States on a full-time basis without current or future sponsorship requirements.
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