At a glanceSummarised by Seekless from the posting.
Must have11
Strong proficiency in SQL (data extraction, cleaning, and validation)
Experience with Python or R for data processing, automation, and tool development
Familiarity with data-quality frameworks, anomaly detection techniques, and statistical validation
Experience working with large datasets and modern data-platform technologies
Knowledge of data-integration patterns (ETL/ELT) and monitoring tools
Familiar with GenAI concepts and prompt engineering and LLM assisted automation
Experienced in building agents with MS Copilot studio or comparable technology
Excellent problem-solving skills — especially in contexts where domain knowledge is limited
Ability to identify trends, irregularities, and outliers using structured and unstructured methods
Strong logical reasoning, abstraction and hypothesis driven thinking
Demonstrate pattern-recognition abilities, translating data signals into actionable insights
Nice to have2
Experience with data-quality monitoring tools (i.e. SODA)
Familiar with data engineering concepts
Skills
SQL
Python
R
Microsoft Copilot Studio
SODA
Job Title: Data Analyst
Role Summary
The Data Analyst plays a critical role in safeguarding the accuracy, integrity, and reliability of large and complex datasets. This role focuses on developing scalable data‑quality capabilities and automated validation frameworks and intelligent anomaly detection — without requiring domain knowledge of the underlying business data.
You will collaborate closely with data engineering, product, and reporting teams, ensuring that data feeding our operational, analytical, and AI‑driven systems is trustworthy, consistent, and ready for decision‑making. The ideal candidate combines technical depth with strong analytical intuition and thrives in data‑intensive, fast‑moving environments.
Key Responsibilities
Data Quality & Validation
•
Perform in depth validation of structured and unstructured datasets to ensure completeness, consistency, accuracy and lineage integrity.
•
Apply statistical techniques, pattern recognition methods, and anomaly detection algorithms to identify irregularities without requiring domain-expertise.
•
Design and implement automated data‑validation checks, including schema validation, threshold monitoring, drift and distribution detection, and rules‑based and machine learning based assessments.
Tooling & Automation
•
Develop scripts, tools, and data pipelines to automate data-quality assessments.
•
Build reusable frameworks to detect data inconsistencies across multiple sources and formats.
•
Integrate validation tools into existing data infrastructure (e.g., ETL/ELT pipelines, data warehouses, APIs and event driven architectures).
Required Skills & Qualifications
Technical Skills
•
Strong proficiency in SQL (data extraction, cleaning, and validation).
•
Experience with Python or R for data processing, automation, and tool development.
•
Familiarity with data‑quality frameworks, anomaly detection techniques, and statistical validation.
•
Experience working with large datasets and modern data‑platform technologies.
•
Knowledge of data‑integration patterns (ETL/ELT) and monitoring tools.
•
Familiar with GenAI concepts and prompt engineering and LLM assisted automation
•
Experienced in building agents with MS Copilot studio or comparable technology
Analytical Skills
•
Excellent problem‑solving skills — especially in contexts where domain knowledge is limited.
•
Ability to identify trends, irregularities, and outliers using structured and unstructured methods.
•
Strong logical reasoning, abstraction and hypothesis driven thinking
•
Demonstrate pattern‑recognition abilities, translating data signals into actionable insights.
Nice to have
Preferred Qualifications
•
Experience with data‑quality monitoring tools (i.e. SODA).