Senior ETL Engineer

Company Introduction

WorldStrides is the global leader in educational travel and experiential learning. The company was founded in 1967 to provide middle school travel programs to Washington, D.C. and has grown to provide a wide range of programs for more than half a million students annually to over 100 countries around the world. WorldStrides offers experiential learning programs in educational travel, performing arts, language immersion, career exploration, service-learning, study abroad, and sports. Each of these experiences helps students to see beyond the classroom and to see the world – and themselves – in new ways.

Job Description

The Senior ETL Engineer designs, builds, and stewards the enterprise data pipelines, lakehouse, and warehouse solutions behind reporting, analytics, and advanced data use cases. They work with an AI-first mindset. AI agents handle much of the hands-on execution, such as code generation, test scaffolding, documentation, and troubleshooting, while the engineer owns the design, the specification, and the review.

Beyond execution, AI is a thinking partner for architecture, trade-off analysis, and connecting the dots across systems. Just as important, this engineer knows when AI isn't the right tool, and holds AI-produced work to the same standard as their own.

This role partners with the business from problem framing through delivery. Rather than executing tickets in isolation, the engineer engages stakeholders early to explore what's possible and shape requirements, applying AI to discovery as well as execution. Key customers include business stakeholders, analysts, and BI teams.

• Data Engineering
• •Design, build, and maintain scalable, high-performance ETL/ELT pipelines supporting analytics, reporting, and advanced data use cases
• •Help shape and consistently apply data engineering standards, best practices, and reusable patterns, in partnership with the data architect and senior engineers
• •Lead data modeling efforts to ensure datasets are optimized for analytics, performance, and usability
• •Evaluate and recommend improvements to existing data architectures, tools, and processes to support growth, scalability, and cost efficiency

• AI-First Engineering
• •Use AI coding assistants and agentic tools as the default for pipeline development, config authoring, test generation, and documentation, shifting personal effort from writing code to specifying, reviewing, and validating it
• •Break work into clear, well-scoped specifications that agents can execute reliably, and review AI-generated output with the same rigor as a peer's pull request
• •Judge when AI is the right tool, and when deterministic automation, templating, or hands-on work is faster, safer, or more reliable
• •Use AI as a thinking partner for architecture decisions, design reviews, root-cause analysis, and pressure-testing assumptions before committing to an approach
• •Contribute to and help steward the team's shared AI toolkit, including agent skills, reusable prompts, context and convention files, and MCP-connected tooling, so each project makes the next one faster
• •Write documentation and standards that both engineers and AI agents can read and use

• Consultative Engagement
• •Engage business stakeholders early, often before a ticket exists, to explore what's possible at a conceptual and logical level and to shape high-level requirements together
• •Balance consultation with delivery: right-size discovery to the problem, and know when to keep exploring versus when to commit and build
• •Use AI to make early conversations concrete, such as quickly prototyping data models, mocking sample outputs, or sketching options for stakeholders to react to
• •Use AI to turn discovery conversations into draft requirements, flag gaps and ambiguities, and generate follow-up questions before work begins
• •Offer alternatives and push back constructively when a request doesn't fit the underlying business need, explaining trade-offs in business terms

• Data Quality, Reliability & Operations
• •Establish and apply data quality, validation, fault-tolerance, and automated testing frameworks across all pipeline stages
• •Design testing approaches where engineers define expectations and data contracts, and agents generate and run tests against them
• •Ensure data integrity, accuracy, freshness, and lineage throughout the data lifecycle
• •Use AI-assisted anomaly detection and troubleshooting to shorten time to resolution, while verifying root causes before acting

• DataOps, Automation & Optimization
• •Contribute to DataOps and CI/CD practices for data pipeline deployment and management
• •Drive automation to reduce manual processes in all stages of development
• •Continuously optimize pipeline performance through query tuning, architectural improvements, and technology enhancements
• •Identify and eliminate waste in data flows, reducing redundant processing and improving overall efficiency
• •Adopt new tools and practices deliberately, favoring depth and standardization over tool churn

• Leadership & Mentorship
• •Lead complex or high-impact initiatives as assigned, collaborating on architecture with the data architect
• •Mentor and coach ETL Engineers, including on effective AI-assisted workflows: writing good specs, reviewing generated output critically, and recognizing where AI falls short
• •Model AI-first habits in code reviews, design discussions, and documentation
• •Share knowledge through documentation, design artifacts, and technical guidance

• Collaboration & Stakeholder Engagement
• •Lead technical discussions to resolve ambiguous or competing data needs
• •Communicate complex concepts clearly to technical and non-technical audiences
• •Collaborate with data scientists, analysts, and BI engineers to adopt data products and improve data usability and trust
• •Build data products that AI agents and AI-assisted analysts can consume reliably, through well-defined semantic models and query contracts
• •Act as a trusted advisor on data feasibility, trade-offs, risks, and prioritization

• Security, Governance & Compliance
• •Ensure data pipelines adhere to enterprise security, privacy, and governance standards
• •Implement access controls, data protection measures, and compliance requirements
• •Apply guardrails to AI and agent usage, including tool permission policies, human approval for writes and pipeline execution, and keeping sensitive customer and financial data within sanctioned tools
• •Support data lineage, auditability, and metadata management initiatives

• Skills & Qualifications
• •Advanced experience with SQL, relational databases, and data warehouse design
• •Strong expertise with Microsoft Fabric and modern data platform concepts
• •Proficiency in Python for ETL development, automation, and testing
• •Deep understanding of data modeling, query optimization, and performance tuning
• •Strong knowledge of DataOps, CI/CD, and automated testing for data platforms
• •Experience designing and supporting production-grade, enterprise-scale pipelines
• •Hands-on production experience with AI coding assistants and agentic development tools, plus familiarity with MCP and agent tool integration
• •A track record of delivering faster with AI without sacrificing quality; examples of your work are welcome
• •Enough working knowledge of the AI tooling landscape to make pragmatic choices, without chasing every new model or tool
• •Strong code review skills, especially spotting subtle logic, security, and performance issues in generated code
• •Experience engaging stakeholders early to shape requirements, not just executing against them
• •Excellent verbal, written, and interpersonal communication skills
• •Proven ability to lead multiple initiatives and influence without formal authority
• •Preferred: experience with metadata- or config-driven pipeline frameworks.

• Education & Experience
• •5+ years of experience in data engineering, BI development, or analytics engineering
• •Bachelor's deg