Data Engineer- Analytics Products

About this role

Job Title: Data Engineer - Analytics Products
Department: Data, Operations, & Finance
Reports to: Manager of Data Analytics Engineering
Location: Remote
Effective Date: July 2026
ABOUT THE DC PUBLIC CHARTER SCHOOL BOARD (DC PCSB)
Every day, we’re doing the work to ensure that 44,000+ public charter school students, families, and communities receive a quality education that makes them feel valued and prepared for lifelong learning, fulfilling careers, and economic security. DC PCSB is an independent agency of the District of Columbia government. Our mission, vision, values, and work are rooted in the principle of ensuring that every DC student has access to a quality education.
ABOUT THE ROLE
The Data Engineer is a core member of DC PCSB’s **Analytics Products team**, which is responsible for building and maintaining the data infrastructure that supports agency reporting, accountability calculations, Enterprise Intelligence, and public-facing analytics products. You will design, implement, and maintain the data pipelines, warehouse models, orchestration workflows, and Data Mart structures that allow PCSB staff, school leaders, and public stakeholders to access reliable, governed, and well-documented data.
This role combines traditional data-engineering expertise — pipelines, orchestration, warehousing, and cloud infrastructure — with close collaboration across Analytics Engineers, Product Managers, and agency program teams. You will help ensure that PCSB’s data architecture supports scalability, auditability, transparency, and long-term maintainability, while maintaining compliance with PCSB’s Data Governance and REDI commitments.
Core Responsibilities

Design, build, and maintain ETL/ELT pipelines using Airflow, SQL, and Python to support analytical workflows, reporting products, and accountability calculations.

Maintain and improve PCSB’s Data Warehouse architecture, including data models, schemas, dependencies, and documentation.

Support the development and reliability of the Data Mart, ensuring that governed, non-PII data are structured for reporting (star schemas), analysis, and Enterprise Intelligence use cases.

Develop and maintain orchestration workflows in Airflow that support recurring data submissions, validation processes, report production, and downstream analytics products.

Implement schema validation and transformation logic to ensure submitted and processed data conform to PCSB standards and Data Mart models.

Monitor and resolve Airflow failures, pipeline errors, and data quality issues in collaboration with Analytics Engineers and Product Managers.

Support accountability-related data infrastructure, including ASPIRE, state assessment reporting, school performance reporting, and potential concurrent accountability calculation frameworks.

Manage cloud-based data infrastructure using AWS RDS, S3, IAM, and related services.

Contribute to a CI/CD environment by writing tests, performing peer code reviews, and ensuring reliable deployment of data pipelines and infrastructure changes.

Collaborate with Analytics Engineers and Product Managers to translate user stories, reporting needs, and policy requirements into reliable data models and automated workflows.

Support PCSB’s Data Governance by maintaining data quality, privacy, transparency, and auditability across systems.

Contribute to the Data Team Handbook and participate in Agile rituals, including sprint planning, reviews, and retrospectives, to improve team processes.

Competencies and Qualifications
Candidates should generally have at least five (5) years of professional experience in data engineering, analytics engineering, software engineering, database development, or a closely related technical role, including substantial experience using SQL and Python in a production or recurring-workflow environment.

Data ingestion and integration: Experience building and maintaining ETL/ELT pipelines that move data from source systems into databases, data warehouses, Data Marts, reporting layers, or analytics products. This includes experience working with structured data, handling source-system inconsistencies, and implementing validation steps.

Data Warehouse modeling and architecture: Experience designing, maintaining, or improving database, warehouse, or analytics data models. Candidates should understand table grain, keys, relationships, dependencies, versioning, and downstream reporting use cases.

Data Mart and semantic modeling: Experience creating governed, report-ready datasets, views, tables, or semantic models that other analysts, Analytics Engineers, or business users rely on for reporting and analysis. Experience with dimensional modeling, star schemas, standardized business definitions, or non-PII reporting layers is helpful.

Workflow orchestration and reliability: Experience developing, maintaining, or troubleshooting scheduled data workflows using Airflow or a comparable orchestration tool. Candidates should be able to monitor recurring workflows, diagnose failures, implement durable fixes, and understand downstream impacts.

Database management and optimization: Experience writing and optimizing SQL for production data systems. Candidates should understand query performance, joins, indexing, migrations, data volume, and how database changes can affect reporting products or downstream users.

Cloud infrastructure and DevOps for data: Experience using cloud infrastructure and deployment workflows to support data pipelines, databases, warehouses, or analytics products. Experience with AWS services such as RDS, S3, IAM, CloudWatch, Lambda, or ECS is helpful. Experience with Git, CI/CD workflows, Docker, or infrastructure-as-code practices is also helpful.

Data governance, quality, and security: Experience implementing data-quality checks, documentation, lineage, privacy protections, access controls, or auditability practices within data pipelines or warehouse models. Experience working with education,…