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Data Analyst (SC Cleared)

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Job Title: Data Analyst (SC Cleared)
Location
Remote (ad hoc travel to London office)
Contract Length
3 Months (with scope to extend)
Start Date
ASAP
IR35
Inside
Interview Process
1 Stage, MS Teams
Clearance
SC
We are supporting a public sector client in hiring a Data Analyst to join a multidisciplinary data team working on a complex, multi-source data platform. This is a hands-on analytical and coding role focused on profiling and assessing data ingested from multiple source systems, identifying where platform functionality does not fully cover or accurately process incoming data, and producing clear, evidence-based findings that directly inform engineering decisions.
The source data is accepted as provided and cannot be modified. The successful candidate will develop a deep understanding of that data, its structure, patterns and edge cases, and work closely with data engineers to prioritise and address coverage gaps and processing issues. A core part of this role involves writing Python code to automate analytical tasks, generate and manipulate YAML configuration files and support broader data engineering workflows.
This role suits an experienced Senior or Mid-Level Data Analyst with at least 5 years of experience, strong hands-on coding ability across Python and SQL, and a sharp, methodical analytical mindset comfortable working at pace in an Agile, multidisciplinary environment.
Reasons to use Rodeo
I’m in my final year doing Economics and I don’t know whether to apply for grad schemes now or do a masters first. What do you think?
Honest answer — it depends on where you want to end up. A lot of top grad schemes (Big 4, civil service, banking) don’t need a masters. Let’s look at the ones you’d be competitive for now, and we can decide if a masters actually adds anything.
Also worth knowing: most autumn 2026 applications are open now. Timing matters more than you think.
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Graduate Consultant — 2026 Scheme
Why you're a good match
StrongYour economics background and your summer at a regional bank line up with what PwC looks for on the consulting scheme. Applications close in four weeks.
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Why you're a good match
You’ve got the grades and the economics background, and your bank internship is exactly the experience this scheme looks for. Apply soon — deadlines close within the month.
Experience fit
Your summer at the bank plus your econometrics coursework map directly to the day-one responsibilities on this scheme — client modelling, market briefings, and deal support.
Only hits
No noise. No "maybe this fits." Just roles with a clear explanation of why they're right — and where to focus when applying.
Key Responsibilities
- Profile data ingested from multiple source systems, developing a thorough understanding of structure, content, patterns and edge cases
- Identify and document gaps where platform functionality does not fully cover, correctly interpret or accurately process the data being received
- Investigate discrepancies between expected and actual data processing outcomes, clearly articulating where platform logic falls short
- Produce clear, evidence-based findings that enable data engineers to prioritise and address coverage gaps and processing issues
- Write Python scripts to automate data analysis tasks, generate YAML configuration files and support repeatable, config-driven workflows
- Develop and maintain SQL queries for exploratory analysis, data profiling and validation of processing outputs
- Define and document data coverage rules, assessment criteria and known platform limitations in collaboration with technical and business teams
- Contribute to documentation of data definitions, data lineage and known processing constraints across the platform
- Participate in Agile delivery ceremonies and contribute to continuous improvement of team practices
Essential Skills
- Strong Python proficiency with the ability to write clean, maintainable scripts for automation, data analysis and YAML file generation and parsing
- Strong SQL skills including complex queries, joins, aggregations, window functions and performance optimisation
- Solid understanding of YAML structure and experience working with YAML-based configuration files using Python libraries
- Understanding of medallion architecture, Azure Data Lake Storage and Databricks, with a focus on Parquet files and Delta tables
- Experience working with Azure cloud services including Azure Synapse Analytics
- Demonstrated experience in data profiling across large, complex or multi-source datasets, identifying patterns, anomalies and edge cases
- Ability to interpret and work with data schemas and data dictionaries
- Strong analytical mindset with exceptional attention to detail and a methodical approach to problem-solving
- Confident communicator able to present technical findings clearly to non-technical stakeholders


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Desirable Skills
- Familiarity with data warehouse or data lake architectures and modern data platform design principles
- Experience working within configuration-driven or parameter-driven data pipeline frameworks
- Knowledge of data governance principles and metadata management practices
- Experience within a regulated or public sector data environment
- Familiarity with CI/CD workflows and version control using Git and Azure DevOps
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