Lead Data Analyst

Company Description
Technology is our how. And people are our why. For over two decades, we have been harnessing technology to drive meaningful change.
By combining world-class engineering, industry expertise and a people-centric mindset, we consult and partner with leading brands from various industries to create dynamic platforms and intelligent digital experiences that drive innovation and transform businesses.
From prototype to real-world impact - be part of a global shift by doing work that matters.

Role Overview
At Endava, we empower organisations to harness the full value of their data through trusted insights, scalable platforms, and human-centred design. With over 500 data and AI professionals globally, our Data & AI Discipline helps clients transform data into strategic assets-driving better decisions, improved operations, and long-term success.
As we grow our Data & AI capability in Australia, we are seeking a Lead Data Analyst to play a critical role in shaping our local delivery. This role combines deep technical expertise in data analysis with a strong understanding of business needs. The Lead Data Analyst is responsible for collecting, transforming, modelling, and visualising data to provide actionable insights that guide business decision-making.
From understanding complex data structures to designing intuitive dashboards, the Lead Data Analyst ensures data is clean, structured, and used effectively. The role also includes collaborating across teams to enhance data processes, improve governance, and strengthen analytical capabilities at client sites.
Key Responsibilities
Data Collection & Integration

• Collect data from diverse sources such as APIs, databases, spreadsheets, and web scraping.
• Design and maintain data systems and databases, identifying and correcting coding issues or data inconsistencies.
  

• Clean and validate data to ensure accuracy and completeness.
• Handle missing values, duplicate records, and formatting issues to prepare high-quality datasets.
  

• Analyse and interpret data structures, schemas, and metadata.
• Infer relationships between datasets and develop logical models to support analysis.
• Design and implement new data structures where needed.
  

• Apply statistical techniques to explore datasets and validate assumptions.
• Perform statistical testing, correlation analysis, and calculations to uncover patterns and relationships.
  

• Create charts, dashboards, and reports that present insights in a compelling and business-relevant way.
• Use storytelling techniques to explain complex data findings clearly and persuasively.
  

• Build and maintain automated and custom reports to inform operational and strategic decisions.
• Deliver insights to stakeholders in a clear, concise, and actionable format.
  

• Work with data engineers, business stakeholders, and product teams to improve data processes and pipeline reliability.
• Contribute to data governance strategies and promote best practices in data quality and accessibility.
  

• Develop a strong understanding of client business domains (e.g. healthcare, financial services, insurance).
• Build models and analyses that align with business priorities and deliver commercial value.
  

• Programming & Querying:
  SQL, Python (pandas, numpy), R
• Data Integration:
  Experience with APIs, flat files, web scraping, ETL pipelines
• Statistical Tools:
  Excel, R, Python (scipy, statsmodels), hypothesis testing
• Visualisation Tools:
  Power BI, Tableau, Looker, Matplotlib, Seaborn
• Data Platforms:
  Relational databases (SQL Server, PostgreSQL), Data Warehouses (Snowflake, Redshift BigQuery), Excel
• Data Modelling:
  Normalisation, schema design, dimensional modelling
  

• Experience working across structured and semi-structured data sources
• Strong understanding of metadata, data cataloguing, and lineage
• Knowledge of data governance frameworks and best practices
  

• Analytical Thinking:
  Able to derive meaningful insights from complex datasets
• Communication:
  Able to present data insights clearly to non-technical stakeholders
• Collaboration:
  Works cross-functionally with engineers, managers, and domain experts
• Leadership:
  Coaches junior analysts and contributes to a culture of data excellence
  

• Experience working in regulated industries such as banking, financial services, insurance, or healthcare is highly valued.
  

• Finance:
  Competitive salary package, share plan, company performance bonuses, value-based recognition awards, referral bonus;
• Career Development:
  Career coaching, global career opportunities, non-linear career paths, internal development programmes for management and technical leadership;
• Learning Opportunities:
  Complex projects, rotations, internal tech communities, training, certifications, coaching, online learning platforms subscriptions, pass-it-on sessions, workshops, conferences;
• Work-Life Balance:
  Hybrid work and flexible working hours, employee assistance programme;
• Health:
  Global internal wellbeing programme, access to wellbeing apps;
• Community:
  Global internal tech communities, hobby clubs and interest groups, inclusion and diversity programmes, events and celebrations.