[Remote] Director, Analytics

Note: The job is a remote job and is open to candidates in USA. Direct Travel is a provider of corporate travel management services serving clients through customized travel programs, technology, and data-driven solutions. The Director of Analytics will lead the strategy, architecture, and delivery of the company’s global analytics platform, including internal and client-facing data products, while building and mentoring a high-performing analytics engineering team.

Responsibilities

• Define and execute the multi-year roadmap for Direct Travel’s global analytics platform, ensuring it serves internal business intelligence needs and external client reporting at scale
• Drive the end-to-end delivery of analytics data products — from pipeline architecture through warehouse design, semantic modeling, and reporting enablement — with a focus toward production quality and reliability
• Own the architecture of embedded analytics applications that integrate directly into our product experiences, providing clients with self-service access to travel data insights
• Establish and enforce standards for data lifecycle management, ensuring clean handoffs from raw ingestion to curated data marts to governed analytics consumption layers
• Serve as a senior technical authority on analytics architecture decisions, including warehouse design (Snowflake, Azure DataLake), semantic modeling (LookML, dbt), and BI platform strategy (Looker, Power BI)
• Provide hands-on guidance on query performance tuning, pipeline optimization, cloud cost management, and system-level diagnostics across cloud providers (AWS, Azure, GCP)
• Lead the design of bespoke data products that integrate analytics and business intelligence platforms in an embedded manner — not just dashboards, but analytics capabilities woven into core product functionality
• Champion modern analytics engineering practices: version-controlled transformations (dbt), Git-based BI content lifecycle management, CI/CD for data pipelines, and infrastructure-as-code deployments
• Evaluate and adopt emerging tools and methodologies, ensuring the team stays current without chasing trends at the expense of stability
• Build, mentor, and lead a high-performing team of analytics engineers who operate across the full stack: data pipelines, warehouse architecture, semantic modeling, embedded analytics, and visualization
• Create an engineering culture rooted in technical excellence, ownership, and continuous improvement — where team members are expected to understand the systems they build end-to-end
• Recruit top-tier analytics engineering talent, with a sharp eye for candidates who combine deep SQL and Python proficiency with cloud platform fluency and a product-oriented mindset
• Establish clear career ladders, growth paths, and technical mentorship programs that retain and develop exceptional engineers
• Foster cross-functional collaboration between analytics engineering, software engineering, product management, and business stakeholders
• Implement and enforce governance frameworks across all analytics data products, including role-based access controls, data lineage, data quality monitoring, and compliance with regulatory requirements (GDPR, SOC 2, PCI where applicable)
• Establish observability and alerting for analytics platforms and pipelines, ensuring issues are detected and resolved before they impact consumers
• Define SLAs for data freshness, dashboard performance, and pipeline reliability — and build the operational practices to consistently meet them
• Partner with executive leadership, product, sales, and operations teams to translate business objectives into analytics capabilities that drive measurable outcomes
• Serve as the primary technical voice in conversations with clients and partners regarding analytics capabilities, data integrations, and reporting roadmaps
• Communicate complex technical concepts clearly to non-technical audiences, building trust and alignment around the analytics platform vision
• Shape the technical architecture from the ground up
• Recruit a nurture a world class team of analytics professionals
• Define what “best-in-class” analytics looks like for a global travel company
• Deliver data products that thousands of clients interact with daily

Skills

• 10+ years of progressive experience in analytics engineering, data engineering, or a closely related technical discipline
• 5+ years in a leadership role directly managing and growing analytics or data engineering teams
• Proven track record of personally delivering production analytics products — both internally facing (executive dashboards, operational reporting, self-service BI) and externally facing (client-facing embedded analytics, multi-tenant reporting platforms, bespoke data products)
• Demonstrated experience recruiting, hiring, and developing analytics engineering talent across multiple levels of seniority
• Deep expertise with cloud data warehouses, specifically Snowflake. Working experience with one or more of: Azure DataLake / Synapse, BigQuery, Redshift
• Strong fluency across at least two major cloud providers (AWS, Azure, GCP) and their respective data services (S3, Glue, Lambda, Azure Functions, GCP Cloud Functions, Dataflow, Pub/Sub)
• Extensive hands-on experience with Looker and LookML modeling
• Senior-level proficiency with Looker or an equivalent (Tableau, Power BI) for advanced reporting, visualization, and stakeholder-facing analytics
• Experience designing and delivering embedded analytics applications — analytics capabilities integrated directly into web applications using frameworks such as React or Vue.js
• Expert-level SQL skills across multiple dialects (Snowflake SQL, BigQuery, PostgreSQL, etc.), including complex query authoring, performance diagnostics, and optimization
• Experience with semantic modeling — designing reusable, governed, performant data models that enable self-service analytics at scale
• Hands-on experience with dbt (Core or Cloud) for transformation orchestration, testing, and documentation
• Familiarity with pipeline orchestration tools such as Informatica, Apache Airflow, Fivetran, or any other
• Strong proficiency in Python for data processing, automation, pipeline development, and scripting
• Working knowledge of additional languages relevant to the data ecosystem (R, Scala, Java) is valued
• Solid understanding of the software development lifecycle, Git-based source control, CI/CD pipelines (GitHub Actions, Jenkins, etc.), and DevOps practices for analytics
• Familiarity with front-end technologies (React, Vue.js) sufficient to guide embedded analytics architecture and collaborate effectively with front-end engineers
• Experience implementing data governance frameworks: RBAC, column-level security, data masking, lineage tracking, and compliance controls
• Demonstrated skill in query and system performance tuning — partitioning, clustering, indexing, caching strategies, and execution plan analysis
• Understanding of data quality frameworks, automated testing for data pipelines, and observability tooling
• Exceptional verbal and written communication skills — able to present analytics strategy to executives, negotiate technical tradeoffs with engineering peers, and explain data concepts to business stakeholders
• A genuinely data-driven mindset with strong diagnostic instincts — the kind of leader who digs into a slow query or a data discrepancy rather than delegating it without context
• Comfort operating in ambiguity and making principled technical decisions when requirements are evolving
• Experience in the travel, hospitality, or logistics industry, particularly with global or multi-regional data at scale
• Experience operating multi-tenant analytics platforms that serve diverse external client bases with varying data access and reporting requirements
• Experience with infrastructure-as-code (Terraform, Docker, Kubernetes) for analytics platform deployment and management
• Familiarity with ML/AI integration into analytics workflows — not as a data scientist, but as a leader who understands how to operationalize predictive models within analytics products
• Advanced degree (M.S. or Ph.D.) in Computer Science, Data Science, Mathematics, or a related quantitative field

Benefits

• Medical benefits
• Dental benefits
• Vision benefits
• Employee rewards and recognition program
• Wellness initiatives
• Sustainability initiatives
• DE&I initiatives
• Mental Health Support

Company Overview

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