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.
Key Responsibilities
Analyse and process large, complex datasets to support ESG regulatory reporting and disclosure requirements.
Support the development, enhancement, and maintenance of data assets used for EU Taxonomy, EBA Pillar 3 ESG, SREP, and other regulatory initiatives.
Perform data exploration, transformation, aggregation, and validation using SQL, Python, Pandas, and PySpark.
Use Jupyter Notebook for exploratory data analysis, prototyping, testing, and validating data transformations.
Investigate data discrepancies, identify root causes, and provide actionable insights to business and technical stakeholders.
Perform data quality checks, reconciliations, and consistency assessments to ensure accurate and reliable reporting outputs.
Collaborate with data engineers to define, implement, and optimise data transformations and ETL workflows.
Gather, analyse, and translate business and regulatory reporting requirements into technical specifications and data transformation logic.
Assess the impact of changing reporting requirements on existing datasets, data pipelines, and deliverables.
Work with large-scale datasets in Apache Spark and distributed computing environments, ensuring efficient data processing.
Support testing, validation, and implementation of new or enhanced data solutions.
Use GitHub for version control, code collaboration, and change management.
Document data mappings, transformation rules, analytical findings, and data lineage.
Communicate findings, risks, and recommendations clearly to both technical and non-technical stakeholders.
Skills & Experience
4-5+ years of experience in Data Analysis, preferably within banking, financial services, or regulatory reporting environments.
Strong proficiency in SQL, including complex joins, CTEs, aggregations, window functions, and data reconciliation.
Strong practical experience with Python, particularly
Pandas for data manipulation, cleaning, aggregation, and analysis.
PySpark for large-scale data processing and transformation.
Working with structured datasets and implementing data validation logic.
Hands-on experience with Jupyter Notebook for Python/PySpark development, exploratory data analysis, prototyping, and data validation.
Good understanding of Apache Spark and distributed computing concepts, including DataFrame operations, partitioning, and performance optimisation.
Solid understanding of data wrangling, ETL processes, data transformations, and data quality controls.
Experience analysing large datasets and investigating data discrepancies across multiple sources.
Familiarity with Git/GitHub, including branching, pull requests, and collaborative development workflows.
Ability to translate business requirements into technical data specifications and work effectively with data engineering teams.
Strong analytical thinking, problem-solving skills, and attention to detail.
Experience collaborating with business stakeholders, data engineers, and cross-functional teams.
Excellent communication skills, with the ability to explain complex data issues clearly.
Fluent English, both written and spoken.
Bachelor's degree in Computer Science, Engineering, Mathematics, Finance, or a related field.
Nice to Have
Experience with Prophecy for visual development, orchestration, and management of Spark-based data pipelines and ETL workflows.
Familiarity with Prophecy's integration with PySpark and Git-based development workflows.
Experience working with ESG data, sustainability reporting, or regulatory disclosures.
Understanding of EU Taxonomy, EBA Pillar 3 ESG disclosures, or SREP.
Knowledge of banking data domains such as credit risk, lending, counterparty information, and financial exposures.
Experience with regulatory reporting data, data lineage, and data governance frameworks.
Familiarity with Spark performance optimisation and large-scale data processing architectures.
Previous experience working in an international banking or consulting environment.
What We Offer
Flexible collaboration model based on a B2B contract.
Opportunity to work on strategic ESG and regulatory reporting initiatives for a leading global financial institution.
Exposure to modern data technologies, including Python, PySpark, Apache Spark, Jupyter Notebook, and distributed computing.
Opportunity to collaborate with experienced data engineers, business analysts, and regulatory reporting specialists.
Challenging projects combining technical data analysis with financial services domain expertise.
Collaborative and supportive team environment.
Access to professional development opportunities and a global network of experts.
Recruitment Process
Screening call with a Recruiter
Capco Hiring Manager / Technical Interview
Client Interview
Feedback / Offer
B2B cooperation in a hybrid model, with the option to provide services from the Kraków office to the extent agreed based on project needs.
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