Job Title: Senior Python Data Engineer – Financial Services

Location: Toronto (Canada, Hybrid)

Job Type: Contract to Hire

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

- Solid 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.
- Robust 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.

Thanks and Regards

NANI

Email: [email protected]

📌 H Position:Senior Python Data Engineer – Financial Services_ Toronto (Canada, Hybrid)
🏢 TechnoSphere
📍 Toronto