Data Platform Engineer This is a vacancy for the 26-27 school year with a target start date of July 1, 2026 Mission: The Data Platform Engineer builds and operates IDEA’s data infrastructure on Snowflake, enabling reliable, scalable access to data that supports analytics, reporting, and research across multiple states. This role designs automated ingestion pipelines, optimizes platform performance and cost, and ensures the data platform functions as a production-grade system for downstream teams. Reporting to the Manager of Data Platform Engineering, this engineer works hands-on with ELT pipelines, infrastructure-as-code, and Snowflake administration while contributing to IDEA’s transition from legacy ETL systems to a modern lakehouse architecture. Supervisory Responsibilities: Individual contributor role with no direct reports. Senior engineers may mentor peers and lead technical initiatives. Location: This is a full-time remote position based in Texas, with preference given to candidates who live in Austin, El Paso, Houston, Permian Basin (Midland/Odessa), Rio Grande Valley, San Antonio, and Tarrant County (Fort Worth), or who are willing to relocate. Travel Expectations: Minimal travel (5–10% annually) for collaboration, training, or critical implementation milestones. What You’ll Do – Accountabilities Essential Duties: - Design, build, and maintain automated ELT pipelines ingesting data from diverse source systems into Snowflake. - Configure and manage cloud-native ingestion tools and custom Python-based pipelines when needed. - Build and maintain Bronze-layer tables with schema evolution handling, audit metadata, and lineage. - Implement ingestion-level validation and monitoring to catch issues early. - Document source configurations, refresh schedules, and troubleshooting procedures. - Partner with Analytics Engineering to ensure ingestion patterns support downstream transformation needs. - Administer Snowflake environments, including databases, schemas, warehouses, access controls, and security settings. Additional Duties and Responsibilities: - Optimize performance and cost through warehouse sizing, clustering, query analysis, and resource monitoring. - Manage Snowflake objects using infrastructure-as-code patterns. - Implement security best practices including RBAC, encryption, auditing, and network policies. - Evaluate and adopt new Snowflake capabilities as appropriate. - Own Terraform-based infrastructure definitions for Snowflake and related platform components. - Automate recurring operational tasks such as provisioning, access grants, and environment setup. - Build CI/CD pipelines for infrastructure changes with testing and safe deployment practices. - Develop reusable templates and modules to accelerate onboarding of new sources and domains. - Maintain clear documentation and runbooks for platform operations. - Implement monitoring and alerting for pipelines, platform health, and performance. - Troubleshoot pipeline failures and platform issues using systematic root-cause analysis. - Embed observability (logging, metrics, alerts) into all production pipelines. - Collaborate closely with Analytics Engineering, DataOps, and Data Governance partners. - Participate in code reviews and design discussions. - Share platform knowledge through documentation, mentoring, and team forums. - Contribute to retrospectives and continuous improvement efforts. Knowledge and Skills – Competencies - Make Strategic Decisions: This team member uses data, feedback, and insights to inform thoughtful decision-making, while considering the impact on their direct reports and team. They communicate decisions with clear rationale and begin to connect their choices to broader team objectives. - Manage Work and Teams: This team member sets clear, measurable goals and regularly reflects on progress, adjusting actions as needed. They prioritize work aligned with their goals using a task management system and consistently meet deadlines through effective time management. - Grow Self and Others: This team member regularly offers affirming and adjusting feedback, maintaining a positive balance that reinforces growth and motivation. They provide transparent, candid performance insights and offer consistent coaching and development aligned with individual goals, supporting both direct reports and cross-functional partners. - Build a Culture of Trust: This team member proactively builds strong personal and professional relationships with individual stakeholders and regularly seeks feedback to improve their work experience. They create a supportive environment where others feel safe to take risks and learn from mistakes without fear of retribution. - Communicate Deliberately: This team member communicates thoughtfully by anticipating potential misunderstandings and providing necessary context to ensure clarity. They leverage structured communication channels to address challenges, ask meaningful questions, and guide conversations toward solutions, while actively listening to the concerns of others. Additional Skills: Required - Hands-on experience administering Snowflake or similar cloud data platforms. - Strong SQL skills for data extraction, validation, and performance tuning. - Experience building and operating automated data pipelines. - Proficiency with Python and scripting for automation and operational tooling. - Experience operating production systems with monitoring and incident response. - Familiarity with infrastructure-as-code and CI/CD concepts. Preferred - Experience with cloud-native ingestion tools (e.g., Fivetran, Airbyte). - Experience with Terraform or similar IaC tooling. - Familiarity with dbt and analytics engineering workflows. - Exposure to orchestration, data quality, or observability tools. - Experience with education, public sector, or regulated data environments. Required Education and Experience: - Bachelor’s degree in a technical field or equivalent practical experience. - 3+ years of experience in data engineering, platform engineering, or related roles. - Demonstrated experience building and operating production data systems. - Hands-on experience with Snowflake or comparable cloud data warehouses. Preferred Education and Experience: - Snowflake or cloud platform certifications. - Experience supporting multi-team data platforms at scale. - Strong Python proficiency beyond basic scripting. Physical Requirements: - Prolonged periods working on a computer and in virtual meetings - Ability to travel domestically via car and air travel to campuses and state office - Flexibility for occasional evening meetings with distributed stakeholders across time zones What We Offer: Compensation & Benefits: Salaries for people entering this role typically fall between $89,600 and $105,300, commensurate with relevant experience and qualifications and in alignment with internal equity. This role is also eligible for performance pay based on organizational performance and goal attainment. Additionally, we offer medical, dental, and vision plans, disability, life insurance, parenting benefits, flexible spending account options, generous vacation time, referral bonuses, professional development, and a 403(b) plan. You can find more information about our benefits at https://ideapublicschools.org/careers/benefits/. * IDEA may offer a relocation stipend to defray the cost of moving for this role, if applicable. Application process: Submit your application online through Jobvite. Please note that applications will be reviewed on an ongoing basis until the position is filled. Applicants are encouraged to apply as early as possible. Learn more about IDEA At IDEA the Staff Experience Team uses our Core Values to promote human connection and a culture of integrity, respect, and belonging for all Team and Family members. Learn more about our Commitment to Core Values here: https://ideapublicschools.org/our-story/#core-values

This description is a summary of our understanding of the job description. Click on 'Apply' button to find out more. Role Description Maleda Tech is seeking an experienced Data Engineer to support the development and expansion of a large-scale data ecosystem used to inform strategic decision-making. In this role, you will design and maintain data pipelines that integrate internal and third-party datasets, enabling advanced analytics, data visualization, and predictive modeling. You will collaborate with cross-functional technical teams, analysts, and business stakeholders to build scalable data infrastructure that supports machine learning models and data-driven insights. The ideal candidate is highly skilled in data engineering, comfortable working with complex datasets, and able to translate technical concepts into clear solutions for non-technical partners. This position is well suited for a data professional who thrives in fast-paced environments and enjoys building foundational data systems that power analytics and machine learning initiatives. Key Responsibilities - Design, build, and maintain ETL pipelines to ingest and transform third-party and internal datasets into enterprise data warehouses. - Develop scalable data pipelines and architecture that integrate multiple data sources and support advanced analytics and machine learning applications. - Configure and manage data integrations across systems including CRM platforms, internal applications, and microservices environments. - Establish and maintain data refresh cadences aligned with business needs and data availability. - Enable self-service data visualization and analytics by preparing clean, structured datasets for reporting tools and dashboards. - Collaborate with engineering, analytics, and data science teams to expand foundational datasets and improve data accessibility. - Integrate diverse data sources such as legislative, regulatory, economic, geospatial, and operational datasets to support advanced analysis. - Communicate complex technical concepts clearly to cross-functional stakeholders and contribute to data strategy discussions. Qualifications - Strong experience in data engineering, data architecture, and analytics infrastructure. - Advanced proficiency in Python and SQL. - Experience designing and managing ETL pipelines and large-scale data pipelines. - Experience working with complex, multi-source datasets, including structured and semi-structured data. - Strong ability to transform raw datasets into formats suitable for analytics and visualization tools. - Experience working with enterprise data warehouse environments and big data systems. - Ability to work independently and proactively identify solutions to technical challenges. - Strong communication skills and the ability to translate technical insights for non-technical stakeholders. Requirements - Experience supporting or building machine learning models. - Experience working with geospatial, regulatory, economic, or legislative datasets. - Background collaborating with cross-functional technical teams including data science and analytics teams. Key Details - Role: Data Engineer - Employment Type: Contract (W2) - Location: United States (Remote) - Start Date: April 7, 2026 - Contract Duration: 12 months (through April 7, 2027) - Schedule: Full-time, 40 hours per week - Work Hours: 8 hours per day, Monday-Friday - Compensation: $92-$107 per hour, depending on experience

• Develop and maintain integration solutions using MuleSoft and Salesforce-native tools (e.g., Salesforce Flow, Data Loader). • Collaborate with stakeholders, Business Analysts, and development teams to define data models, integration flows, and technical specifications. • Design and implement scalable, secure data architectures aligned with DHS and federal compliance standards (e.g., NIST, FISMA). • Lead data migration efforts from legacy systems to Salesforce, ensuring data integrity, accuracy, and completeness. • Perform data mapping, cleansing, transformation, and validation to support migration and integration workflows. • Write and optimize ETL scripts (e.g., SQL, Apex, Python) for data extraction, transformation, and loading. • Support the Authorization to Operate (ATO) process by contributing to cybersecurity documentation and ensuring compliance with DoD and federal security standards. • Provide technical guidance on data governance, data quality, and integration best practices. • Create and maintain technical documentation, including migration plans, integration specifications, and gate review deliverables. • Participate in Agile ceremonies (e.g., sprint planning, daily stand-ups) to align with team goals and timelines. • Troubleshoot and resolve data or integration issues post-deployment to support ongoing system maintenance.

• Design, build, and maintain scalable and reliable batch and real-time ETL/ELT data pipelines using cloud services such as GCP Dataflow, Cloud Functions, Pub/Sub, and Cloud Composer. • Architect and implement robust data infrastructure capable of handling high-volume data ingestion and processing. • Develop and manage our central data warehouse in Google BigQuery. • Design and implement data models, schemas, and table structures optimized for performance, scalability, and long-term maintainability. • Write clean, efficient, and maintainable SQL and Python code to transform raw data into curated, analysis-ready datasets. • Build reliable transformation workflows that support analytics, reporting, and data science initiatives. • Monitor, troubleshoot, and optimize data infrastructure to ensure high performance, reliability, and cost efficiency. • Implement BigQuery best practices, including partitioning, clustering, query optimization, and materialized views. • Build and maintain curated data models that serve as the “source of truth” for business intelligence and reporting. • Ensure data is optimized and readily accessible for BI tools such as Looker and other analytics platforms. • Implement automated data quality checks, validation rules, and monitoring frameworks to ensure the integrity and reliability of data pipelines and warehouse systems. • Establish processes for data governance, observability, and lineage tracking. • Work closely with software engineers, data analysts, and data scientists to understand their data requirements and provide the necessary infrastructure and data products. • Lead and support client and stakeholder communication, working with enterprise clients to translate business needs into scalable data solutions. • Partner with product teams and leadership to ensure that technical data solutions align with business strategy and client expectations. • Take ownership of data platforms and architecture decisions, helping shape the future direction of our analytics and data infrastructure. • Identify opportunities to improve data reliability, automate workflows, and generate new insights through data. • Contribute to a collaborative, high-performing engineering culture with strong communication and teamwork.