Senior Associate - Python Developer

Job Purpose

Design and build reusable Python frameworks that span front‑end integrations, backend/middle‑tier services, and cloud execution. You’ll translate complex post‑trade data flows into robust data models, stored procedures, and regression automation, and own CI/CD—including scripts to run AutoSys/batch jobs and stored procedures across environments. The role also requires strong data‑quality ownership: verifying data accuracy, completeness, duplication handling, referential integrity, and conformance to business rules and transformation logic; reviewing and validating schema structures, data types, constraints, metadata, and lineage; designing and executing data‑validation test cases; and performing detailed source‑to‑target comparisons using SQL or automation frameworks - identifying data anomalies and document defects.

The ideal candidate brings pragmatic engineering discipline, capital‑markets data intuition, and a builder’s mindset.

Key Responsibilities

• Python engineering & reusable frameworks
• Build modular Python packages (data processing, API clients, orchestration adapters), publishable to internal artifact repositories; enforce code quality, testing, and documentation standards.
• Develop backend services/APIs (e.g., FastAPI/Flask) and CLI tools to support front‑end, middle‑tier, and cloud workflows; implement resilient error handling, observability hooks, and secure secrets usage.
• Data modeling, SQL & stored procedures
• Design relational schemas and write/optimize complex SQL (windowing, CTEs, partitioning); author and refactor stored procedures (SQL Server/Oracle/Postgres) with attention to edge cases and performance.
• Build data‑validation utilities that compare large datasets across environments and produce diffs for regression packs.
• Post‑trade domain, test automation & regression
• Map post‑trade flows (allocations, clearing/settlement, confirmations, reconciliations) into datasets, rules, and assertions for repeatable regression.
• Read and translate stored‑proc logic into automated test scripts; build a central repository of reusable checks integrated into CI/CD.
• Nice to have: familiarity with trade/blotter platforms (e.g., ION) or similar post‑trade systems.
• CI/CD, DevOps & environment promotion
• Design and operate multi‑stage CI/CD pipelines (Azure DevOps/Jenkins/GitLab) for code, data artifacts, and SQL deployables; implement approvals, rollbacks, environment strategy, and quality gates (lint, SAST/DAST, tests).
• Containerize services where appropriate; integrate with AKS/Kubernetes or serverless jobs, and wire up metrics/alerts for runtime health.
• Scheduling, batch & operationalization
• Script the execution of AutoSys/batch jobs and stored procedures across dev/UAT/prod; add run‑books, logging, and guardrails; enable reliable, auditable promotions through environments.
• Data validation
• Perform comprehensive data validation across all stages of the data lifecycle
• Validate data accuracy, consistency, completeness, timeliness, and adherence to business rules and transformation logic.
• Identify and analyze data anomalies, integrity issues, schema mismatches, duplicate records, null handling problems, and referential integrity violations.
• Execute SQL queries to validate data rules, transformation outputs, and pipeline results; independently troubleshoot data discrepancies and log defects with clear impact analysis.
• Design and maintain reusable test cases, validation scenarios, and automated data verification scripts where applicable.
  
  

Validate metadata, schema structures, data types, constraints, primary/foreign key relationships, and data lineage compliance.

Key competencies

5–7 years of hands‑on software engineering with Python (incl. packaging, virtual envs, unit/integration testing); strong use of libraries such as pandas, SQL Alchemy/pyodbc, and asyncio/celery for pipelines and services.

Expert SQL skills and stored‑proc development (SQL Server/Oracle/Postgres), query tuning, and execution‑plan analysis for large datasets.

Proven experience designing CI/CD pipelines and automating promotion (code + data + DB objects) with Azure DevOps/Jenkins/GitLab; strong Git practices and code‑review hygiene.

Comfort with schedulers (AutoSys/Control‑M/Airflow) and shell/Python scripting for batch orchestration; familiarity with secrets management and environment configuration.

Domain understanding of post‑trade data flows and how to encode them into repeatable regression checks.

 Perform end-to-end data validation across the lifecycle, ensuring accuracy, completeness, consistency, referential integrity, and adherence to business rules and transformation logic.

Execute SQL-based source to target checks, validate schemas (data types, constraints, metadata, lineage), and identify anomalies such as duplicates, null issues, and mismatches; investigate and log defects with clear impact analysis.

 Design and maintain reusable data validation test cases and automated verification scripts to support scalable, repeatable quality checks.

Good analytical skills to understand post‑trade or financial datasets and translate them into repeatable validation scenarios (Capital Markets domain knowledge is good to have).

Experience working with test case management tools (e.g., JIRA, XRay) and following agile development practices.

Ability to contribute to QA processes, including manual testing, data validation, and regression testing for critical applications.

Strong attention to detail, good communication skills, and a willingness to learn and grow within a data engineering/testing environment.

Good to have

Cloud exposure (Azure/AWS), containers/Kubernetes, infrastructure as code (Bicep/Terraform), and DevSecOps gates (Sonar/Mend/ZAP).

Experience with capital‑markets platforms (e.g., ION blotter integrations) and messaging/API patterns in trading data stacks.

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