Data Platform Engineer (26-27)

IDEA Public Schools•El Paso, TX

89d•$89,600 - $105,300•Remote

About The Position

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. This is an individual contributor role with no direct reports. Senior engineers may mentor peers and lead technical initiatives. 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. Minimal travel (5–10% annually) for collaboration, training, or critical implementation milestones.

Requirements

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.
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.

Nice To Haves

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.
Snowflake or cloud platform certifications.
Experience supporting multi-team data platforms at scale.
Strong Python proficiency beyond basic scripting.

Responsibilities

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.
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.