Mid Data Analyst (Banking experience)

At Capco Poland, we're not just another consultancy—we're the dynamic force driving digital transformation in the financial world. As a global leader in technology and management consulting, we help clients tackle complex challenges across banking, payments, capital markets, wealth, and asset management.

Our culture is fast-paced, flexible, and entrepreneurial. We move quickly, think creatively, and put people at the center of everything we do. We're passionate about delivering value to our clients while creating opportunities for our people to grow and thrive.

We're proud to be:

• Leaders in banking, payments, capital markets, wealth, and asset management consulting

• Champions of an agile, innovative, and collaborative work environment

• Committed to attracting and developing exceptional talent

We are looking for an experienced Data Analyst to support ESG (Environmental, Social, and Governance) data and regulatory reporting initiatives within a leading global financial institution.

In this role, you will contribute to the development, enhancement, and maintenance of data assets supporting key regulatory and sustainability disclosures, including EU Taxonomy, EBA Pillar 3 ESG disclosures, and SREP-related requirements.

You will work closely with business stakeholders, regulatory reporting teams, data engineers, and technical specialists to analyse complex datasets, derive meaningful insights, investigate data quality issues, and translate business requirements into reliable data solutions.

The role combines hands-on data analysis, data transformation, and stakeholder collaboration, with a strong focus on Python, PySpark, SQL, and distributed data processing, leveraging tools such as Jupyter Notebook and GitHub.

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  Analyse and process large, complex datasets to support ESG regulatory reporting and disclosure requirements.

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  Support the development, enhancement, and maintenance of data assets used for EU Taxonomy, EBA Pillar 3 ESG, SREP, and other regulatory initiatives.

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  Perform data exploration, transformation, aggregation, and validation using SQL, Python, Pandas, and PySpark.

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  Use Jupyter Notebook for exploratory data analysis, prototyping, testing, and validating data transformations.

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  Investigate data discrepancies, identify root causes, and provide actionable insights to business and technical stakeholders.

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  Perform data quality checks, reconciliations, and consistency assessments to ensure accurate and reliable reporting outputs.

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  Collaborate with data engineers to define, implement, and optimise data transformations and ETL workflows.

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  Gather, analyse, and translate business and regulatory reporting requirements into technical specifications and data transformation logic.

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  Assess the impact of changing reporting requirements on existing datasets, data pipelines, and deliverables.

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  Work with large-scale datasets in Apache Spark and distributed computing environments, ensuring efficient data processing.

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  Support testing, validation, and implementation of new or enhanced data solutions.

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  Use GitHub for version control, code collaboration, and change management.

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  Document data mappings, transformation rules, analytical findings, and data lineage.

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  Communicate findings, risks, and recommendations clearly to both technical and non-technical stakeholders.