Director, Analytics

Direct Travel

11h

About The Position

Direct Travel is in the midst of a defining data transformation, and this role sits at the center of it. We are seeking a Director of Analytics — a hands-on technical leader who will own the strategic direction, architecture, and delivery of our global analytics platform. This platform serves both internal stakeholders and thousands of clients worldwide, making it one of the most visible and impactful investments the company is making. This is not a role for someone who manages from a distance. We need a leader who has personally built and shipped data products — someone who has architected pipelines, modeled semantic layers, stood up embedded analytics applications, and knows what it takes to deliver production-grade analytics at scale. You will lead a team of analytics engineers who work across the full data lifecycle: from raw data ingestion and warehouse architecture to semantic modeling, self-service reporting, and bespoke client-facing data products. The right candidate has a demonstrable track record of delivering both internal and externally facing analytics products, understands the engineering rigor required to operate a multi-tenant analytics platform, and can recruit, mentor, and elevate a high-caliber team while continuing to provide deep technical guidance.

Requirements

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.

Nice To Haves

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.

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.