Director, Analytics

Director, Analytics

$215,000-$235,000 + 15-20% bonus

Overview

A growing enterprise is undertaking a significant transformation of its data and analytics capabilities and is seeking a senior analytics leader to define and execute its analytics platform strategy.
This role is ideal for a hands-on technical leader with deep experience building and delivering production-grade analytics solutions. The successful candidate will oversee the architecture, development, and operation of a global analytics ecosystem serving both internal stakeholders and external customers. In addition to leading a high-performing analytics engineering team, this individual will provide technical direction across data warehousing, semantic modeling, embedded analytics, governance, and platform operations.

Key Responsibilities

Analytics Platform Strategy & Delivery

• Define and execute the long-term roadmap for a global analytics platform supporting internal reporting and customer-facing analytics.
• Drive delivery of analytics products across the full data lifecycle, including ingestion, transformation, warehousing, semantic modeling, and reporting.
• Design and oversee embedded analytics capabilities integrated into digital products and applications.
• Establish standards for data architecture, data marts, semantic layers, and governed reporting environments.
• Ensure platform scalability, reliability, maintainability, and performance.

Technical Leadership

• Serve as the senior technical authority for analytics architecture and engineering decisions.
• Guide warehouse architecture, cloud data platform strategy, semantic modeling, and BI tool adoption.
• Provide hands-on support for performance tuning, optimization, cloud cost management, and operational troubleshooting.
• Promote modern analytics engineering practices including version control, CI/CD, testing, infrastructure automation, and analytics lifecycle management.
• Evaluate emerging technologies and recommend solutions that balance innovation with operational stability.

Team Leadership & Development

• Build, mentor, and lead a team of analytics engineers across data engineering, modeling, reporting, and analytics product development.
• Establish a culture of technical excellence, accountability, collaboration, and continuous improvement.
• Recruit and develop high-calibre technical talent.
• Define career development frameworks, mentorship opportunities, and technical growth pathways.
• Foster collaboration across engineering, product, business, and operational teams.

Data Governance & Operations

• Implement governance frameworks covering security, access controls, compliance, lineage, and data quality.
• Establish monitoring, observability, and alerting for analytics platforms and pipelines.
• Define and manage service-level objectives for data freshness, platform availability, and reporting performance.
• Ensure adherence to regulatory and security requirements where applicable.

Stakeholder Engagement

• Partner with executive leaders and business stakeholders to translate strategic objectives into analytics solutions.
• Act as the primary technical representative for analytics capabilities with customers, partners, and internal teams.
• Communicate complex technical concepts effectively to both technical and non-technical audiences.
• Align analytics initiatives with organisational goals and measurable business outcomes.

Required Qualifications

Leadership & Experience

• 10+ years of experience in analytics engineering, data engineering, or related technical disciplines.
• 5+ years of experience leading and developing analytics or data engineering teams.
• Proven experience delivering both internal business intelligence solutions and external customer-facing analytics products.
• Demonstrated success recruiting, mentoring, and growing technical teams.

Data Warehousing & Cloud Platforms

• Deep expertise with modern cloud data warehouses, particularly Snowflake.
• Experience with one or more additional data platforms such as Azure Synapse, data lakes, BigQuery, or Redshift.
• Strong knowledge of at least two major cloud ecosystems (AWS, Azure, and/or GCP) and associated data services.

Analytics & Business Intelligence

• Extensive experience with Looker and semantic-layer modelling.
• Advanced proficiency in a modern BI platform such as Looker, Tableau, or Power BI.
• Experience building embedded analytics solutions integrated into web applications.

Data Modeling & Transformation

• Expert-level SQL across multiple database platforms.
• Experience designing governed, reusable semantic models for self-service analytics.
• Hands-on experience with dbt for transformation, testing, and documentation.
• Familiarity with orchestration and ingestion tools such as Airflow, Fivetran, Informatica, or comparable technologies.

Programming & Engineering

• Strong Python programming skills for analytics engineering, automation, and pipeline development.
• Knowledge of additional languages such as Scala, Java, or R is beneficial.
• Solid understanding of Git, CI/CD pipelines, DevOps practices, and software development methodologies.
• Working familiarity with frontend technologies sufficient to collaborate on embedded analytics initiatives.

Governance & Performance

• Experience implementing data governance controls including RBAC, security policies, lineage, masking, and compliance requirements.
• Expertise in performance tuning, optimisation, and query diagnostics.
• Knowledge of data quality testing, observability tooling, and monitoring frameworks.

Communication & Leadership Style

• Excellent written and verbal communication skills.
• Strong analytical and problem-solving mindset with a willingness to investigate technical issues directly.
• Ability to lead effectively in fast-changing environments with evolving priorities.

Preferred Qualifications

• Experience within a large-scale service, operations, logistics, transportation, or hospitality-related environment.
• Background supporting multi-tenant analytics environments serving external customers.
• Experience with infrastructure-as-code and containerisation technologies such as Terraform, Docker, and Kubernetes.
• Understanding of how machine learning or AI solutions can be operationalised within analytics products.
• Advanced degree in Computer Science, Data Science, Mathematics, Engineering, or a related quantitative field.