Data Analyst & Analytics Engineer: Professional Portfolio
Professional Profile
I am a Data Analyst and Analytics Engineer with over four years of experience, primarily in international banking at BNP Paribas. My background focuses on automating reporting processes, building reliable data workflows, improving data quality, and translating business requirements into practical data solutions.
I have extensive experience working with sensitive and regulated data, where accuracy, documentation, controls, and stakeholder alignment are critical. My technical toolkit includes Python, SQL, SAS, VBA, Dataiku, and Dremio to make data more reliable, automated, and usable for business teams. I am currently seeking a role to apply this data and governance mindset in a broader business context, specifically at the intersection of HR, IT, and analytics.
Project Experience: Financial Servicing Automation
I automated a sensitive financial servicing process related to portfolio sales at BNP Paribas. The bank sold debt recovery contracts to external investors, but we managed the direct debit servicing. As the number of investors grew, the manual process became risky and unscalable.
- Execution: I collaborated with Finance, Portfolio Sales, and Reporting teams to define business rules and controls. I built an end-to-end workflow in Dataiku using Python and SQL.
- Results: The solution ingested, cleaned, and normalized data, performed cross-checks against internal databases, and delivered customized files via secure SFTP. I also automated a Tableau dashboard for monitoring.
- Impact: This resulted in reduced manual work, improved controls, lower operational risk, and an estimated efficiency saving of three FTEs.
Data Quality and Scalability
To ensure data quality, I implemented multiple layers of control: structure checks during ingestion, normalization rules, and cross-checks against internal databases. For scalability, I parameterized the workflow logic, allowing for the addition of new investors without rebuilding the core process.
Motivation and Career Goals
I am drawn to PMI due to its global scale and ongoing business transformation. I am particularly interested in the synergy between talent data, HR, IT, and analytics. My goal is to create trustworthy data foundations that empower leaders to make informed decisions. In five years, I aim to be a senior professional with deep expertise in people analytics and modern data platforms.
Technical Concepts and Definitions
Data Architecture & Lifecycle
- Data Architecture: The framework for organizing data from source to consumption, ensuring reliability and scalability.
- Ingestion to Consumption: The full data lifecycle, including cleaning, transformation, validation, and delivery via dashboards or models.
- ETL vs. ELT: ETL (Extract, Transform, Load) transforms data before loading; ELT (Extract, Load, Transform) leverages modern cloud platforms like Snowflake to transform data after loading.
Governance & Quality
- Data Governance: The set of rules, responsibilities, and controls (ownership, access, lineage) that ensure data integrity and compliance.
- Data Lineage: The ability to trace data from its source through all transformations to its final output.
- Role-Based Access Control (RBAC): Restricting data access based on user roles, which is vital for sensitive HR data.
Strengths and Development
My core strengths include adaptability, structured problem-solving, and bridging the gap between technical delivery and business needs. Regarding areas for improvement, I have learned to balance my tendency for extreme technical rigor with the need for delivery speed by implementing earlier iteration checkpoints.