Senior Data Analyst

This range is provided by Made Tech. Your actual pay will be based on your skills and experience — talk with your recruiter to learn more.

Base pay range

Direct message the job poster from Made Tech

Founder at S.H.I.F.T | Leading the Shift in Talent Strategies and Mindsets

Made Tech wants to positively impact the country's future by using technology to improve society, for everyone. We want to empower the public sector to deliver and continuously improve digital services that are user‑centric, data‑driven and freed from legacy technology. A key component of this is developing modern data systems and platforms that drive informed decision‑making for our clients. You will also work closely with clients to help shape their data strategy.

As a Senior Data Analyst, you may play one or more roles according to our clients' needs. The role is very hands‑on and you'll support as a senior contributor role for a project, focusing on:

• Data analysis and reporting: Conducting in-depth data analysis, generating reports, and providing actionable insights for client projects.
• Data and BI visualisation: Producing BI dashboards using industry‑standard tools - Power BI, Tableau, Quicksight etc.
• Client interaction: Collaborating with clients to understand their needs, translating these into analytical solutions, and presenting findings in a clear, actionable manner.
• Mentoring junior analysts, leading data‑focused projects, and setting best practices in data analysis.

You’ll need to have a drive to deliver outcomes for users. You’ll make sure that the wider context of a delivery is considered and maintain alignment between the operational and analytical aspects of the engineering solution.

Key responsibilities

• Application of analytical techniques: Proficiency in applying various analytical methods such as statistical analysis, data mining, and qualitative analysis. Ability to select and apply appropriate techniques based on the context and research data.
• Synthesis of research data: Experience in synthesising research data to present actionable insights and solutions. Ability to articulate the impact of their analysis on decision‑making and problem‑solving.
• Engagement with sceptical colleagues: Effective communication and persuasion skills to engage and gain buy‑in from sceptical colleagues. Ability to foster collaboration and address concerns to ensure adherence to best practices.
• Advisory and critique skills: Capability to advise on the choice and application of analytical techniques and critique colleagues’ findings to ensure high standards in data analysis.

Data Management

• Understanding of data sources and storage: Knowledge of various data sources, data organisation, and storage practices. Commitment to maintaining data integrity and accessibility.
• Advocacy for data governance: Experience in advocating for data governance standards and influencing team adherence to data quality practices.
• Continuous improvement: Ability to communicate and implement continuous improvements in data management practices through documentation, training, and regular team engagement.
• Toolset management: Proficiency in defining and supporting common toolsets for data management, ensuring efficiency and seamless integration.
• Automation of data management (desirable): Experience in automating data management activities to streamline processes and increase accuracy.
• Compliance with data governance policies: Understanding and ensuring compliance with data governance policies, maintaining data security and ethical standards.

Data modelling, cleansing, and enrichment

• Data modelling expertise: Proficient in conceptual, logical, and physical data modelling. Ability to adhere to data modelling standards and best practices.
• Data cleansing and standardisation: Experience in resolving data quality issues and ensuring data accuracy through cleansing and standardisation techniques.
• Use of data integration tools: Skilled in using ETL tools for data integration and storage. Ensures data interoperability with other datasets.
• Collaboration with data professionals: Experience collaborating with other data professionals to improve modelling and integration standards and patterns.
• Interpretation of requirements: Ability to interpret data visualisation requirements and create meaningful, visually appealing representations tailored to the audience.
• Proficiency in visualisation tools: Experience with tools such as Tableau, Power BI, and Python libraries like Matplotlib and Seaborn. Knowledge of selecting appropriate visualisation types.
• Application of visualisation standards: Application of design principles to create clear, accurate, and accessible visualisations. Awareness of accessibility considerations.
• Mentorship in visualisation: Experience in reviewing and advising junior members to improve the quality and efficiency of data visualisations.

Data Quality Assurance, Validation, and Linkage

• Data quality assurance: Experience in implementing processes for data quality assessment and improvement, including data profiling, cleansing, and standardisation.
• Data validation and linkage: Ability to perform data validation checks and integrate data from various sources to ensure consistency and accuracy.