Role Overview
We are seeking an experienced Senior Python Data Engineer to join a large-scale technology initiative within the financial services industry.
The successful candidate will combine strong Python and data-engineering expertise with experience delivering complex enterprise solutions in highly regulated environments. Experience within a large global bank or financial institution is strongly preferred, particularly in technology supporting Finance, Accounting, Product Control, or Capital Markets functions.
The role requires a senior hands-on engineer capable of designing reliable, scalable, and well-controlled data solutions while working effectively with both technology teams and financial subject-matter experts.

Key Responsibilities
• Design, develop, and maintain robust data-processing and integration solutions using Python.
• Build scalable pipelines for the ingestion, transformation, normalization, validation, and distribution of financial data.
• Define and implement standardized, reusable data models and interfaces across heterogeneous source systems.
• Develop reliable processing solutions that maintain data accuracy, consistency, lineage, and traceability.
• Implement data-quality controls, reconciliation processes, exception handling, and error-management mechanisms.
• Design solutions that promote loose coupling between data producers and downstream consumers.
• Ensure compliance with enterprise requirements for auditability, security, resiliency, data governance, and regulatory controls.
• Develop comprehensive unit, integration, regression, and data-quality tests.
• Participate in code reviews and contribute to engineering standards, reusable frameworks, and development best practices.
• Analyze and troubleshoot complex data and production issues spanning multiple systems.
• Collaborate with architects, business analysts, Finance and Accounting SMEs, trading technology teams, and other engineering groups.
• Translate business and financial requirements into maintainable technical solutions.
• Produce clear technical documentation covering data flows, interfaces, transformations, controls, and operational procedures.
• Participate throughout the delivery lifecycle, including analysis, design, estimation, implementation, testing, deployment, and production support.
Required Experience and Skills
• Strong professional experience in Python development, particularly in data-intensive enterprise applications.
• Significant experience as a Data Engineer, Software Engineer, or Data Platform Engineer on large and complex technology programs.
• Strong knowledge of Python software-engineering practices, including modular design, object-oriented development, dependency management, testing, logging, and exception handling.
• Experience building ETL/ELT pipelines, data-integration services, or large-scale data-processing platforms.
• Strong SQL skills and experience working with relational databases.
• Experience designing and implementing canonical or standardized data models.
• Strong understanding of data transformation, mapping, validation, reconciliation, and data-quality principles.
• Experience integrating heterogeneous systems using databases, files, APIs, messaging platforms, or other enterprise integration technologies.
• Experience designing solutions where reliability, recoverability, idempotency, traceability, and deterministic processing are important.
• Strong automated testing practices, including unit and integration testing.
• Experience with modern software-delivery practices, including Git, CI/CD, automated testing, and controlled deployment processes.
• Experience working within formal SDLC, change-management, and production-support frameworks.
• Strong analytical and problem-solving skills and the ability to work effectively with both technical and business stakeholders.
Financial Services Experience
• Demonstrated experience working within banking, capital markets, or another highly regulated financial-services environment.
• Experience delivering technology solutions within a large global financial institution is strongly preferred.
• Understanding of financial products, trading environments, and front-to-back financial data flows is highly desirable.
• Strong preference will be given to candidates with direct experience in Product Control, Finance Technology, Accounting Technology, or closely related functions within a major investment bank or global financial institution.
• Experience integrating or processing data across trading, Finance, Product Control, Accounting, General Ledger, or financial-reporting platforms is particularly valuable.
• Understanding of financial controls, reconciliation, accounting data, data lineage, and audit requirements within regulated institutions.
Relevant domain experience may include trade lifecycle processing, Product Control, P&L processing, general ledger integration, sub-ledgers, accounting feeds, financial reporting, reference data, or regulatory reporting.

Preferred Technical Experience
Experience with one or more of the following would be advantageous:
• Pandas, Polars, PySpark, or comparable Python data-processing frameworks.
• REST APIs and service-oriented architectures.
• Messaging and event-driven technologies such as Kafka.
• High-volume batch and/or streaming data-processing architectures.
• Oracle, PostgreSQL, SQL Server, Snowflake, or comparable enterprise data platforms.
• AWS, Azure, or GCP.
• Docker and Kubernetes.
• Enterprise scheduling and orchestration platforms.
• Data-lineage, metadata-management, and data-quality tools.
• Monitoring and observability platforms.
• Performance optimization of high-volume data-processing applications.
Architecture and Engineering Principles
The candidate should be comfortable applying principles including:
• Canonical data models and standardized enterprise data representations.
• Loose coupling between data producers and consumers.
• Metadata-driven and configuration-driven processing.
• Idempotent and restartable data pipelines.
• End-to-end data lineage and traceability.
• Reconciliation and control frameworks.
• Schema evolution, backward compatibility, and versioned data contracts.
• Resilient and recoverable processing architectures.
• Separation of business logic from source-specific transformation logic.