Data Analyst | Wholesale Banking Advanced Analytics @ING Hubs Romania

Description

Discover ING Hubs Romania

ING Hubs Romania offers 130 services in software development, data management, non-financial risk & compliance, audit, and retail operations to 24 ING units worldwide, with the help of over 2000 high-performing engineers, risk, and operations professionals.

We started out in 2015 as ING’s software development hub, then steadily expanded our range to include more services and competencies. Now we provide borderless services with bank-wide capabilities and operate from our office in Bucharest.

Our tech capabilities remain the core of our business , with more than 1800 colleagues active in Data Management, Touchpoint Channels & Integration, Core Banking, and Global Products.

We enjoy a flexible way of working and a highly collaborative environment, where fair and constructive feedback is encouraged.

For us, impact isn't a perk. It's the driver of our work. We are guided and rewarded by a shared desire to make the world a better place, one innovative solution at a time. Our colleagues make it their job to do impactful things and they love doing it in good company. Do you?

The mission

The Data Analyst is accountable for delivering analytics and insights that generate measurable improvements in operational efficiency and customer experience. The role owns the end-to-end development of analytics solutions from requirements gathering and data sourcing through data integration, analytical modelling, insight generation, and data visualization.

Working closely with Product Managers, Tribes and Process Leaders, the Data Analyst translates business priorities into data-driven opportunities and supports the execution of optimization strategies for business decision-making. The role brings together business context, analytical expertise, and clear communication to deliver reliable, scalable, and actionable analytics products in collaboration with Data Engineers, Data Scientists, and other stakeholders.

Your day to day

Data Analysis and Visualization

Use SQL and Python (Pyspark) to analyze large and complex datasets, produce reports, and generate statistics that support business decisions.

Identify trends, patterns, anomalies, and opportunities, and translate findings into practical recommendations.

Develop and maintain dashboards and visualizations using tools such as Power BI and Cognos to present data clearly to stakeholders.

Explore data sources across different systems, assess availability and suitability, and support data quality and profiling activities.

Document analytical methods, assumptions, definitions, and results to promote transparency and reuse.

Business Analysis

Partner with stakeholders to understand business objectives, requirements, constraints, and success measures.

Translate business needs into well-defined analytical questions and data-driven solutions aligned with broader organizational goals.

Communicate findings clearly through concise narratives, reports, presentations, and dashboards tailored to technical and non-technical audiences.

Collaborate with Data Engineers and Data Scientists to design and deliver robust, scalable, and adaptable analytics products.

Leadership and Continuous Improvement

Guide and mentor junior and mid-level analysts, promoting analytical rigor, knowledge sharing, and consistent delivery standards.

Stay current with emerging technologies, analytical practices, and industry trends.

Adopt new tools and methodologies as needed and contribute to continuous improvement across the analytics team.

What you’ll bring to the team

Required Experience and Capabilities

3 -5 years of professional experience in data analytics, data visualization, statistics, or a related field.

Strong SQL skills and hands-on experience using Python, especially PySpark, for data analysis and transformation.

Experience with a data visualization or business intelligence platform such as Power BI, Tableau, Cognos, or Superset.

Sound knowledge of data reporting, statistical analysis, and standard analytical techniques.

Ability to work effectively with large datasets and apply analytical, statistical, and quantitative problem-solving skills.

Excellent written and verbal communication skills, with the ability to influence and align diverse stakeholders.

Ability to understand predictive models used in machine learning and AI projects.

Preferred Experience

Experience working with data pipelines and modern data platforms.

Data analytics experience in financial services or banking.

Hands-on experience with dbt for data transformation and modeling.

Practical understanding of machine learning and artificial intelligence concepts.

Education and Language

Bachelor's degree in Mathematics, Economics, Computer Science, Information Management, Engineering, Statistics, or related discipline. An advanced degree (e.g., Master, PhD) or a strong academic record with advanced coursework in Math, Statistics, and data analytics is a strong plus.

Advanced proficiency in English.

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