The Travel Product org is seeking a versatile Data Engineer to join our Product Strategy and Analysis team. In this hybrid role, you will play a critical part in shaping our analytics infrastructure and capabilities. You’ll work closely with product managers, engineers, and operations teams to design and build analytics-ready data models, automate data workflows, and uncover impactful insights that drive our product and operational decisions.
Your work will span the full data lifecycle—developing and optimizing data models, building engaging dashboards, and analyzing our platform’s performance and user behavior. You will also leverage our in-house AI platform to develop and improve data-driven prompts and automations that enhance self-service analytics.
This is a hands-on role and ideal for someone who thrives with both data solutions and translating data into actionable insights. If you enjoy building high-quality data assets, partnering cross-functionally, and making an immediate impact on product strategy, we’d love to meet you!
What You’ll Do:
- Design, build, and maintain robust data models, tables, and views in our analytics data warehouse (using dbt/Snowflake) to support product and operational analytics.
- Develop and automate analytics-layer data pipelines and ensure data quality with appropriate testing and monitoring.
- Partner with product managers, engineers, and business stakeholders to understand requirements, translate business needs into data solutions, and help define and track key metrics.
- Deliver thoughtful analyses on large, complex datasets to uncover actionable insights for product and operations improvement.
- Develop and maintain dashboards and reports in Thoughtspot to monitor KPIs, product usage, and operational performance.
- Consolidate and harmonize data across different business units and acquisitions, ensuring consistent and reliable data definitions for reporting.
- Work directly with our in-house AI platform, supporting or developing new tools to boost analytics and automation capabilities.
- Contribute to documentation and training for business users to support self-service access to data tools and reporting.
- Identify and implement opportunities to streamline and automate analytics workflows and processes in partnership with the broader team.
- Primarily work on-site 3-4 days per week as part of our collaborative Product Strategy and Analysis team.
What We’re Looking For:
- 2-3+ years of relevant experience in data engineering, analytics engineering, or advanced data analytics roles, ideally supporting product or operational analytics.
- Advanced proficiency in SQL and experience with data modeling, dbt, and cloud data warehouses (Snowflake preferred).
- Experience developing analytics-layer data pipelines, tables, and views, and ensuring data quality for reporting and analysis.
- Hands-on experience with BI/data visualization tools (Thoughtspot preferred), including dashboard/report development and self-service enablement.
- Experience analyzing complex, high-volume datasets and delivering clear, actionable insights.
- Hands-on experience with Python for data analysis and/or to interact with platforms or AI tools; prompt/AI development is a plus.
- Effective written and verbal communication skills, with the ability to convey technical information to cross-functional partners.
- Collaborative mindset and ability to thrive within a hybrid team, working on-site 3-4 days per week.
Nice to Have:
- Experience experimentation frameworks, or statistical analysis (e.g., regression, significance testing).
- Experience working in a product-centric team or fast-paced environment.
- Stakeholder management experience, especially with Product, Engineering, and Operations teams.
- Familiarity with engineering best practices (versioning, testing, monitoring)
Navan uses AI-assisted Automated Employment Decision Tool (Metaview) to assist with evaluating resumes against job qualifications for this role. All final decisions are made by human recruiters and hiring managers.
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Skills Required
- 2-3+ years of relevant experience in data engineering, analytics engineering, or advanced data analytics
- Advanced proficiency in SQL
- Experience with data modeling and dbt
- Experience with cloud data warehouses, preferably Snowflake
- Experience developing analytics-layer data pipelines, tables, and views
- Experience ensuring data quality for reporting and analysis
- Hands-on experience with BI or data visualization tools, preferably ThoughtSpot
- Experience developing dashboards and reports
- Experience analyzing complex, high-volume datasets and delivering actionable insights
- Hands-on experience with Python for data analysis or platform and AI tool integration
- Effective written and verbal communication skills
- Ability to collaborate cross-functionally in a hybrid team with on-site work 3-4 days per week
- Experience with experimentation frameworks or statistical analysis, such as regression and significance testing
- Experience working in a product-centric or fast-paced environment
- Stakeholder management experience with Product, Engineering, and Operations teams
- Familiarity with engineering best practices including versioning, testing, and monitoring
What the Team is Saying
What We Do
Navan (Nasdaq: NAVN) is the leading all-in-one business travel, payments, and expense management platform that makes travel easy for frequent travelers. From finding flights and hotels to automating expense reconciliation, with 24/7 support along the way, Navan delivers an intuitive experience travelers love and finance teams rely on. See how Navan customers benefit and learn more at navan.com.
Why Work With Us
At Navan, we’re never satisfied with the status quo, and we know breakthrough ideas come from diverse perspectives. We are committed to cultivating a workplace that reflects the diversity of the customers we serve while fostering leadership and innovation.
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Navan Offices
Hybrid Workspace
Employees engage in a combination of remote and on-site work.
In-person connections is the foundation of Navan, the connections forged through face-to-face interactions improve company culture and what we can achieve together. We operate on a hybrid working model, which we define as four days a week in-office.
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