Senior Consultant | Data Analyst

The team:

Engineering helps empower and drive mission-critical solutions whether we need to modernize existing systems or implement new technology products and platforms. Through innovation, we improve financial performance, accelerate new digital businesses and fuel growth.

Your work profile:

We are looking for a highly analytical and curious Data Analyst who can go beyond producing reports and dashboards to extract the story hidden inside complex datasets.

The ideal candidate has an exceptional ability to tease out patterns, anomalies, relationships, trends and business insights from data.

SQL and Python are critical skills for this role. You will work extensively with Databricks for data exploration and analysis and increasingly use LLMs to accelerate data discovery, analysis and productivity.

We value analytical thinking and business judgment over simply knowing a particular BI tool.

1. Data Exploration & Insight Generation:

• Explore large and complex datasets to uncover patterns, trends, anomalies, relationships and opportunities.
• Demonstrate an exceptional ability to ask: "What is really happening in this data, and why?"
• Move beyond surface-level descriptive analysis to identify the underlying drivers of business outcomes and investigate unexpected movements, outliers, and key metrics.
• Connect information across multiple datasets to develop a holistic understanding of a business problem.
• Translate raw data into clear, concise and actionable insights.

2. Business Problem Solving:

• Work with business stakeholders to understand ambiguous and complex problems.
• Break broad business questions into well-defined analytical questions and hypotheses.
• Determine what data is actually relevant to answering a question.
• Challenge assumptions and identify gaps in existing analyses.
• Distill complex analyses into the few insights that genuinely matter for decision-making.
• Communicate not just what happened, but why it happened, what it means, and what should happen next.

3. SQL & Databricks:

• Write advanced, efficient SQL for data exploration, transformation and analysis in Databricks.
• Work with large datasets and understand joins, aggregations, window functions, CTEs, subqueries and complex transformations.
• Investigate data quality and understand data lineage and relationships between datasets.
• Develop reusable analytical datasets and queries.
• Optimize queries where required and demonstrate an understanding of computational efficiency.
• SQL is a core requirement of this role.

4. Python & Analytical Programming:

• Use Python extensively for exploratory data analysis, statistical analysis, data transformation and visualization.
• Work comfortably with libraries such as Pandas, NumPy, Matplotlib/Seaborn and other relevant analytical libraries.
• Automate repetitive analytical workflows.
• Use Python when SQL alone is insufficient for deeper analysis or statistical investigation.
• Python is a core requirement of this role.

5. LLM-Augmented Analytics:

• Use LLM-based tools such as Databricks Genie to accelerate data exploration, querying and analysis.
• Effectively use LLMs to generate hypotheses, explore datasets, formulate SQL, investigate patterns and accelerate repetitive analytical tasks.
• Understand that LLM output must be validated rather than blindly trusted.
• Develop effective prompting and analytical workflows that combine human judgment + LLM capabilities + data.
• Identify opportunities where AI can substantially improve analyst productivity.
• We expect the modern analyst to be AI-augmented, not AI-dependent.

6. Data Visualization & Storytelling:

• Select appropriate charts and visualizations based on the analytical question.
• Have a strong understanding of charts, graphs, distributions, trends and visual perception.
• Design visualizations that make complex information easy to understand.
• Avoid misleading or unnecessarily complicated visualizations.
• Build analytical narratives that allow stakeholders to quickly understand the key message.
• Present findings effectively to both technical and non-technical audiences.

7. Dashboard & UI/UX:

• Design dashboards that prioritize clarity, usability and decision-making rather than simply displaying large numbers of KPIs.
• Understand principles of information hierarchy, layout, interaction and visual design.
• Think about the user's workflow when designing analytical interfaces.
• Translate analytical requirements into intuitive dashboard experiences.
• UI/UX expertise is a major plus, particularly experience designing effective analytical dashboards.

8. Metrics & Statistical Thinking:

• Define and evaluate meaningful business metrics and KPIs.
• Understand concepts such as:
• Mean, median and variance
• Distributions and percentiles
• Sampling
• Correlation
• Confidence intervals
• Statistical significance
• Hypothesis testing
• Basic experiment/evaluation concepts
• Understand the difference between signal and noise.
• Evaluate whether observed changes in metrics are meaningful or potentially due to random variation.
• Apply appropriate statistical reasoning to business problems without unnecessarily overcomplicating the analysis.

Key skills required:

• Education - Bachelors in Engineering
• Strong SQL skills - this is non-negotiable.
• Strong Python skills for data analysis.
• Hands-on experience working with Databricks or comparable data platforms.
• Exceptional analytical and problem-solving ability.
• Ability to work with large, messy and imperfect datasets.
• Ability to translate ambiguous business problems into analytical questions.
• Strong data exploration and investigative skills.
• Strong understanding of data visualization and chart selection.
• Good understanding of basic statistics and metric evaluation.
• Excellent communication skills - particularly the ability to distill complex analysis into simple conclusions.
• Curiosity and a willingness to challenge assumptions.
• Ability to independently investigate a problem rather than waiting for perfectly defined requirements.
• Experience with Databricks Genie or other LLM-based analytical tools.
• Experience building analytical dashboards.
• Strong UI/UX understanding, especially for data products and dashboards.
• Experience with Power BI, Tableau, Plotly, Streamlit or similar visualization tools.
• Experience working with product, operations, supply chain, commercial or other business datasets.
• Experience automating analytical workflows using Python.
• Understanding of data modeling and data engineering concepts.