Management Consultant

About the role

The role combines consulting advice with technical delivery to help clients solve complex data challenges. You will work across energy, finance, public sector, and technology, media and telecoms. The position supports the growth of the Data, AI, Solutions & Engineering practice.

Data roles turn raw information into decisions. Analysts query databases and build dashboards. Data scientists build models that predict outcomes. Data engineers build the pipelines that move and store data. All three work closely with business teams and need a mix of statistics, coding, and communication. Nearly every modern company runs on data teams, from startups to banks. A strong portfolio of past analyses matters more than degrees in many hiring decisions.

Key facts

What you'll do

Clients use cloud data warehouses, data lakes, and data platforms to drive digital transformation, and you will define and implement these on premise or cloud architectures.

Maturity assessments of clients' data capabilities are performed by you, and recommendations are provided to align with strategic objectives.

Technology blueprints are built and clients are advised on data and analytics technology options through your work.

Identifying risks and mitigations for complex data programmes is part of your work, along with supporting transition to modern cloud-based infrastructures using patterns such as APIs and events.

You help build the skills of the Data, AI, Solutions & Engineering team so they can solve client challenges.

Requirements

A bachelor's degree across any discipline is required; a master's in a technical discipline is helpful but not essential.

The ability to design and maintain robust data pipelines and architectures on modern delivery frameworks must be demonstrated by you.

Experience with cloud technologies such as AWS, GCP, or Azure, and with big data platforms like Spark, Hive, and Databricks is required.

Working knowledge of relational databases and NoSQL database technologies is necessary.

Understanding and designing CI/CD pipelines is part of your responsibilities for solution delivery.

Competence in SQL and at least one modern programming language such as Python is required.

Beneficial experience with BI visualisation tools is considered, though not mandatory.

A drive to continuously learn and develop technical and consulting skills throughout your career should be shown by you.

Comfort with international travel within the EU/UK for up to 30% of the time is necessary.

Practical notes

Applications undergo review by a human Talent Acquisition team, and hiring decisions are not made solely by automated systems.

Typical interview steps

Data interviews commonly include a SQL or coding exercise, a statistics question, and a case study. Candidates may be asked to design a metric, interpret an experiment, or build a small model. Some companies give a take-home analysis. Expect questions about past projects and the business impact of your work. Interviewers often evaluate how you communicate uncertainty and business impact, not only the math. Bringing a clean write-up of a past analysis to the interview is well received.

Good to know

Questions to ask

Good questions to ask the employer in the interview: what does success look like in the first six months, how is the team structured, what is the current biggest challenge, and how are decisions made. Asking about growth paths and the review process is also well received. Employers expect questions, and good ones show preparation.

Career growth

Data careers grow toward senior analyst, staff data scientist, or data engineering lead. Many professionals specialize in machine learning, analytics, or infrastructure. Cross-functional work with product and engineering teams becomes more important at senior levels. The field changes quickly, so continuous learning is part of the job. Professionals who can translate numbers into decisions tend to advance fastest.