{"id":2570643,"url":"https://alion.io/job/worldstrides-senior-etl-engineer","title":"Senior ETL Engineer","company":{"id":685357,"name":"WorldStrides","domain":"worldstrides.com","url":"https://alion.io/company/worldstrides-2","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Greenhouse","truth_index":{"grade":"B","score":82,"open_postings":7,"ghost_share":0,"stale_share":0.714,"repost_share":0,"time_to_fill_p50_days":45,"computed_at":"2026-10-15T05:46:00Z"}},"role":"Data Science","role_family":"Data Science","seniority":"senior","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"explicit","locations":["United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":{"min":100000,"max":150000,"currency":"USD","period":"year","gross":null,"usd_annual":150000},"salary_estimate":null,"experience_years_min":5,"visa_sponsorship":true,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"CI/CD","optional":false},{"name":"ETL/ELT","optional":false},{"name":"LLM Guardrails","optional":false},{"name":"Microsoft Fabric","optional":false},{"name":"Model Context Protocol","optional":false},{"name":"Python","optional":false},{"name":"SQL","optional":false}],"status":"live","first_seen_at":"2026-10-14T19:13:27Z","employer_posted_date":"2026-10-14","last_verified_at":"2026-10-15T11:02:24Z","board_verified":true,"closed_at":null,"days_open":0,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":0},"description":"Company Introduction\nWorldStrides 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.\nJob Description\nThe 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.\nBeyond 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.\nThis 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.\nData Engineering\nDesign, build, and maintain scalable, high-performance ETL/ELT pipelines supporting analytics, reporting, and advanced data use cases\nHelp shape and consistently apply data engineering standards, best practices, and reusable patterns, in partnership with the data architect and senior engineers\nLead data modeling efforts to ensure datasets are optimized for analytics, performance, and usability\nEvaluate and recommend improvements to existing data architectures, tools, and processes to support growth, scalability, and cost efficiency\nAI-First Engineering\nUse 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\nBreak 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\nJudge when AI is the right tool, and when deterministic automation, templating, or hands-on work is faster, safer, or more reliable\nUse AI as a thinking partner for architecture decisions, design reviews, root-cause analysis, and pressure-testing assumptions before committing to an approach\nContribute 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\nWrite documentation and standards that both engineers and AI agents can read and use\nConsultative Engagement\nEngage 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\nBalance consultation with delivery: right-size discovery to the problem, and know when to keep exploring versus when to commit and build\nUse AI to make early conversations concrete, such as quickly prototyping data models, mocking sample outputs, or sketching options for stakeholders to react to\nUse AI to turn discovery conversations into draft requirements, flag gaps and ambiguities, and generate follow-up questions before work begins\nOffer alternatives and push back constructively when a request doesn't fit the underlying business need, explaining trade-offs in business terms\nData Quality, Reliability & Operations\nEstablish and apply data quality, validation, fault-tolerance, and automated testing frameworks across all pipeline stages\nDesign testing approaches where engineers define expectations and data contracts, and agents generate and run tests against them\nEnsure data integrity, accuracy, freshness, and lineage throughout the data lifecycle\nUse AI-assisted anomaly detection and troubleshooting to shorten time to resolution, while verifying root causes before acting\nDataOps, Automation & Optimization\nContribute to DataOps and CI/CD practices for data pipeline deployment and management\nDrive automation to reduce manual processes in all stages of development\nContinuously optimize pipeline performance through query tuning, architectural improvements, and technology enhancements\nIdentify and eliminate waste in data flows, reducing redundant processing and improving overall efficiency\nAdopt new tools and practices deliberately, favoring depth and standardization over tool churn\nLeadership & Mentorship\nLead complex or high-impact initiatives as assigned, collaborating on architecture with the data architect\nMentor and coach ETL Engineers, including on effective AI-assisted workflows: writing good specs, reviewing generated output critically, and recognizing where AI falls short\nModel AI-first habits in code reviews, design discussions, and documentation\nShare knowledge through documentation, design artifacts, and technical guidance\nCollaboration & Stakeholder Engagement\nLead technical discussions to resolve ambiguous or competing data needs\nCommunicate complex concepts clearly to technical and non-technical audiences\nCollaborate with data scientists, analysts, and BI engineers to adopt data products and improve data usability and trust\nBuild data products that AI agents and AI-assisted analysts can consume reliably, through well-defined semantic models and query contracts\nAct as a trusted advisor on data feasibility, trade-offs, risks, and prioritization\nSecurity, Governance & Compliance\nEnsure data pipelines adhere to enterprise security, privacy, and governance standards\nImplement access controls, data protection measures, and compliance requirements\nApply 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\nSupport data lineage, auditability, and metadata management initiatives\nSkills & Qualifications\nAdvanced experience with SQL, relational databases, and data warehouse design\nStrong expertise with Microsoft Fabric and modern data platform concepts\nProficiency in Python for ETL development, automation, and testing\nDeep understanding of data modeling, query optimization, and performance tuning\nStrong knowledge of DataOps, CI/CD, and automated testing for data platforms\nExperience designing and supporting production-grade, enterprise-scale pipelines\nHands-on production experience with AI coding assistants and agentic development tools, plus familiarity with MCP and agent tool integration\nA track record of delivering faster with AI without sacrificing quality; examples of your work are welcome\nEnough working knowledge of the AI tooling landscape to make pragmatic choices, without chasing every new model or tool\nStrong code review skills, especially spotting subtle logic, security, and performance issues in generated code\nExperience engaging stakeholders early to shape requirements, not just executing against them\nExcellent verbal, written, and interpersonal communication skills\nProven ability to lead multiple initiatives and influence without formal authority\nPreferred: experience with metadata- or config-driven pipeline frameworks.\nEducation & Experience\n5+ years of experience in data engineering, BI development, or analytics engineering\nBachelor's degree in a technical or analytical discipline required\nWork Perks\nComprehensive health benefits: medical, dental, vision, life, AD&D, accident, critical illness, disability insurance, FSA/HSA options with employer HSA contributions, and a generous 401(k) match.\nGenerous paid time off including 11 paid company holidays, 25 PTO days the first year (prorated), 1 paid volunteer day, 4 mental health days, and bereavement\nProfessional development support, including up to $5,250 annually for tuition reimbursement and up to $1,000 for certifications annually\nOpportunities for paid and discounted travel\nFlexible work schedule providing on-site, remote, and hybrid office opportunities\nEncouraged participation in a wide variety of Employee Resource Groups\nEmployee Assistance Program (EAP) and wellness resources, including fitness centers at select locations\nPaid parental, caregiver, and disability leave\nTeam Member Discount Program\nEngaging, collaborative, and fun work environment\nThe salary range listed for this position represents WorldStrides’ good-faith estimate of the base compensation for the role at the time of posting. Actual compensation will be determined based on factors such as relevant experience, skills and qualifications, geographic location, internal equity, and market conditions. The range does not include other forms of compensation or benefits that may be offered as part of the total rewards package.\nCompensation Range\n$100,000—$150,000 USD\nWorldStrides, a global organization, is committed to educate and serve communities worldwide. Our commitment is fueled by the passion of our team members and partners to make experiential learning accessible, while also being socially, environmentally, and ethically responsible. Together, we accomplish this by investing in initiatives to promote inclusion, diversity, and sustainability.\nAs an Equal Opportunity Employer, WorldStrides is committed to building a diverse workforce, supported by an environment that promotes inclusion and belonging. All qualified applicants will receive consideration for employment without regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state, or local laws.\nWorldStrides will only employ those who are legally authorized to work in the United States. This is not a position for which sponsorship will be provided. Individuals with temporary visas such as E, F-1, J-1, H-1, H-2, L, B, J or TN, or who need sponsorship for work authorization now or in the future, are not eligible for hire. 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