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Sobre esta Vacante MIGx AG Ubicación: Barcelona, Catalonia, España Modalidad: On-site Industria: IT Services and IT Consulting Descripción del Puesto 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.
Here's how to pick the right one and stand out in your application.
144.883Jobs
31.687IN
81%EN
That number is real. WorkMundi's database shows 144,883 open engineer roles across the world. India has the most with 31,687 jobs, followed by the United States with 30,084. If you just finished reading one job ad and felt paralyzed by choice, you're not alone—but this scale is actually an advantage. It means you can afford to be selective.
Start by geography and language. The majority of engineer ads—117,837 of them—have the job posting text written in English. Use that as one filter, but remember: the ad text language tells you nothing about whether the role actually requires you to speak English day-to-day. Read the job description carefully. Then check which countries have the volume you're targeting. Singapore, Poland, and Australia round out the top five after India and the US.
Next, learn who's hiring. Accenture has posted 2,801 engineer roles. andurilindustries, speechify, and jobgether are also actively recruiting. If you're applying to one of these names, research their hiring patterns and interview style before you apply. That homework pays off.
When you interview, expect the question every engineer hears: 'Tell me about a time you had to debug a problem that wasn't in your job description.' Have a specific story ready—not a general one. Name the tools, the deadline pressure, and what you learned. Hiring managers listen for whether you see problem-solving as part of the role itself, not a favour.