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
1) 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.
2) 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.
3) 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.
4) 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.
5) 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.
6) 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, SQLAlchemy/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.
Soft Skills:
1.Excellent problem-solving, analytical, and communication skills (written and verbal).
2.Strong organizational abilities with meticulous attention to detail.
3.Ability to work independently with minimal supervision and collaboratively in cross-functional teams.
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