Data Analyst & QA Engineer

About the profile

We're looking for a Data Analyst and QA Engineer (Hybrid role) to join our Data and AI Engineering team. This role is ideal for those with a quality-first mindset – passionate about profiling, testing, and safeguarding the integrity of data, while also uncovering patterns that drive business decisions and turning validated data into clear, data-driven narratives.

You’ll safeguard data quality across life sciences projects – profiling datasets, running structured tests, and documenting findings to an audit-ready standard – while also working with cross-functional teams to translate validated data into clear, evidence-based insights for technical and non-technical stakeholders alike.

Responsibilities

• Profile and analyse large datasets – assessing data quality (nulls, cardinality, distributions, uniqueness) and identifying trends, anomalies, and opportunities for optimisation or improvement.
• Design, execute, and document structured test plans for data pipelines and dashboards, including defect tracking and reproducible results.
• Support data quality and governance initiatives by documenting data lineage, assumptions, methodologies, and incident write-ups with the traceability expected in a regulated environment.
• Build ETL pipelines and data transformations using Python to prepare and process structured and unstructured data.
• Perform exploratory data analysis (EDA) and statistical analysis using Python (pandas, NumPy, SciPy, scikit-learn).
• Collaborate with Data Engineers and Business Stakeholders to understand analytical requirements and translate them into technical specifications.
• Present findings and recommendations to technical and non-technical audiences with clarity and impact.
• Maintain dashboards and reusable data models in Power BI to support self-service reporting where needed.

What We’re Looking For

We're committed to building a skilled and diverse engineering team. If you care deeply about writing clean code, delivering resilient systems, and collaborating across disciplines — this role is for you.

Requirements - Must have

• Data profiling: systematically assessing data quality across nulls, cardinality, distributions, min/max ranges, type consistency, and uniqueness.
• Test planning & execution: defining test scope, writing test cases traceable to acceptance criteria, tracking defects, and coordinating UAT/regression cycles.
• Strong documentation habits: process runbooks, incident write-ups, and analysis summaries, written with the traceability and version control expected in a regulated environment.
• Experience with SQL for querying and manipulating relational databases.
• Solid proficiency in Python for data analysis and manipulation (pandas, NumPy, matplotlib/seaborn), including exploratory data analysis and statistical techniques to uncover insights.
• Demonstrable data-driven mindset: ability to ask the right questions, validate assumptions, and support decisions with evidence.
• Working knowledge of Power BI for building or maintaining dashboards and reports.
• Understanding of data visualization principles and ability to communicate insights clearly to diverse audiences.
• Team-first mindset and experience in agile environments (Scrum or Kanban).

Requirements - Nice to have

• Experience with clinical trial data, healthcare analytics, or regulated industry datasets.
• Familiarity with advanced Python libraries (scikit-learn, stats models) for statistical modelling and machine learning.
• Knowledge of Git/version control for collaborative analytics projects.
• Experience with cloud platforms (Azure, AWS, or GCP) and cloud-native analytics tools such as Microsoft Fabric.
• Exposure to agile analytics methodologies or analytics engineering principles.
• Background with data governance, data quality frameworks, master data management, or GxP and other regulated industry standards.
• Exposure to QA testing practices and defect tracking tools (e.g. Jira, Azure DevOps), plus basic anomaly detection methods (e.g. z-scores, IQR) for flagging unexpected values.

Seniority Level

• Middle / Senior.

Languages

• English: B2 / C1 level.
• Spanish and/or Catalan language skills are nice to have.

What we offer

• Hybrid work model and flexible working schedule that would suit night owls and early birds.
• 25 days of annual leave.
• Opportunities for career development and the opportunity to shape the company's future.
• An employee-centric culture directly inspired by employee feedback – your voice is heard, and your perspective encouraged.
• Different training programs to support your personal and professional development.
• Work in a fast-growing, international company.
• Friendly atmosphere and supportive Management team.