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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