Data Analyst

Our client in the financial services sector is looking for an intermediate data analyst to join their team on a contract basis. The Data Analyst role is a key role to drive the strategic objective of becoming an Insights Driven Company. The role exists to transform data into actionable insights that support strategic decision-making, optimize operations, and drive business growth. By identifying trends, patterns, and opportunities, this role enables the company to make informed, data-driven decisions.

KPA 1:

1. Strategy alignment

Unlock value from data in multiple business areas by providing accurate and timeous analysis. Use data driven insights to influence strategic goals to move the bank towards becoming a data driven company.

2. Data storytelling and communication

Simplify analysis and present at a PLH/divisional executive level to change business plans and roadmaps.

3. Identify Opportunities

Proactively identifies future data issues and challenge assumptions from a data perspective when building new products & services. Less defined and less obvious opportunities for improvement.

4. Data and Stakeholder enablement:

Create supporting tools/environments (e.g. KPIs, Reports, forecasting, scenario analysis) to make smarter and faster decisions from data for both technical and non-technical audiences.

KPA 2

1. Project delivery / management:

Delivery is a combination of individual contribution and delivering through others. Collaborate with analysts, engineers and scientists.

Work with a wide variety of stakeholders and teams. Actively engage with stakeholders and external partners. Manage stakeholder expectations, negotiating requirements and resolving conflict in data projects.

2. System thinking

Work as the bridge between technical teams to translate business questions to data requirements.

Understand the business processes and how data flows into the warehouse. Should be involved in the design of new processes and ensure the design caters for data needs at the end of projects with little to no data rework needed.

3. Understanding the client and Business

Understand multiple business areas, from client, warehouse data and process perspective. Should have a wide network of individuals in other data roles to help soundboard approaches and share knowledge to drive efficiencies.

Influence decisions through data and analysis rather than position.

1. Work smarter not harder

Challenge requests to ensure time and effort goes to the right problems. Mentors less experienced colleagues to operate in the same way. Probe (often vague) requirements and understand what business needs instead of what is being asked.

Work with business to identify areas for improvement through data and balance short term vs long term requests with business.

KPA 3

1. Way of work:

Owns the project, planning, scoping, timelines and delivery. Support less experienced colleagues with their projects.

2. Data Analysis and Critical thinking

Applies advanced understanding of data analytical skills and advanced statistical knowledge to understand the options for different analysis. (ranging between SQL, Python, Visualisation etc. to solve different parts of the problem). Build 'simple' predictive models with support from data scientists on the design side and by following the DS lifecycle.

Apply standards and processes (do proper testing, sense-checking, ask for help) so that output is of high quality to enable decision making on the analysis.

3. Quality of delivery

Complete projects on time with a high level of accuracy. Delivery is well documented, repeatable and can be implemented into production with very little support and rework. Mentors and upskill less experienced colleagues on improving quality of work.

4.  Efficiency of delivery

Push back on requests that won't solve longer term needs. Cultivate a wide network of individuals in other data roles to help soundboard approaches and share knowledge to drive efficiencies. Uses and improve existing processes and/or data assets instead of "re-inventing the wheel"

Apply understanding of GenAI and its application in the data environment. Support the building of custom requirements to get value from these tools with support from seniors.

KPA 4

1. Analytical way of work

Update the analytical process and own the improvement of the process. Collect input from multiple teams and collate it into a defined roadmap for improvement. Keep up to date with the latest technical development in the field and use it to improve overall way of work.

1. Technical support

Mentor less experienced colleagues to follow Capitec standards and processes for code development and deployment. Help define and build new datamarts that can be used by multiple teams. Ensure internal product knowledge is captured and shared with wider analytical community.

1. Data Quality

Identify gaps in current processes and data assets to reduce the number of future incidents and workaround needed. Challenge the designs of new projects to ensure data needs are met in the final delivery – required to reduce the amount of code rewriting needed after a new product or service goes live.

1. Centre-of-Master (CoM) support

Mentor and upskill less experienced colleagues in a variety of skills (SQL, python, data visualisation etc.) to improve standards. Lead the knowledge sharing portion of the CoP events.

Support the CoM with day-to-day activities (interviews, code reviews, SME guidance).

Qualifications

Minimum:

A bachelor's degree in finance, economics, mathematics, statistics, computer science, or a related field and 3+ years experience or 5+ years work experience in related analytical role.

Ideal:

Masters in relevant field and 2+ years work experience.

Certifications in data analysis such as advanced courses in SQL , data visualization tools or cloud work environments (Azure, AWS).

Knowledge

Technical Knowledge:

Minimum

Able to find, extract and transform data through variety of tools and platforms.

Solid understanding of data warehouses and how to work in them, e.g. understanding dimensional modelling, data marts and ETL processes.

Familiarity with advanced features of data visualization tools and processes and experience in data storytelling.

Knowledge of various types of analysis to identify patterns and trends or anomalies.

Ideal

Advanced knowledge of programming languages (R/Python) for exploratory data analysis tasks and building basic predictive models.

Understanding of fundamental statistical concepts and their application in data analysis.

Knowledge of version control for code versioning and collaboration.

Experience

3-5+ years of experience in data analysis, with a significant portion in the financial services or retail sector.

Proven track record of leading data analysis projects and driving business impact through data insights

In using advanced data analysis tools and software (e.g., SQL, Python, R, Tableau, Power BI).

Experience in performing complex data analysis and statistical modeling.

Experience working with, guiding and providing subject matter expertise to cross-functional teams (e.g., finance, marketing, IT) to understand business needs and provide data-driven insights.

Of communicating and presenting findings and recommendations to non-technical stakeholders.

Understanding of various financial products and services, industry trends, industry regulations and compliance requirements and their impact on financial data analysis.

Design thinking experience and understanding how to build data assets for re-usage at scale.

Skills

Functional Skills

Data Warehouse knowledge, should understand advanced concepts and impact..

Proficiency in SQL to write complex queries, optimizing query performance, and working with large datasets. Experience in building data assets (tables, marts, semantic models) that enables re-usage at scale.

Expertise in advanced Excel functions.

Proficiency in creating advanced visualistions and dashboards using tools like Power BI, or similar.

Advanced programming skills - Python or R, including data manipulation libraries (e.g., pandas, numpy) and data visualization libraries (e.g., matplotlib, seaborn).

Strong understanding of statistical methods and their application in data analysis.

Version control (git) for code versioning and collaboration.

Organisational Skills

Analytical Thinking: Strong analytical skills to interpret complex data and provide actionable insights.

Effective Communication: Ability to communicate complex data findings to non-technical stakeholders.

Project Management: Ability to manage multiple projects and meet deadlines.

Problem-Solving: Advanced problem-solving skills to address complex data issues.