Python Developer

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.

Candidate Profile

The ideal candidate is a senior hands-on engineer comfortable operating within the complexity, governance, and delivery processes of a large financial institution.

They should be able to understand both detailed implementation challenges and the broader architecture in which their solutions operate. They will be expected to work closely with multiple technology and business stakeholders, challenge unclear requirements constructively, and develop solutions that remain scalable and maintainable as business requirements, data sources, and consumers evolve.

A strong appreciation for data correctness, control, explainability, and operational reliability is essential.

Roles and 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.

Education
Bachelor's degree in Computer Science, Engineering, Mathematics, Finance, or a related discipline, or equivalent professional experience.

Preferred Skills:

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.

Relevant postgraduate qualifications or financial-industry certifications are advantageous but not required.