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Job Description & How to Apply Below
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 Auto Sys/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, Dev Ops & environment promotion
Design and operate multi‑stage CI/CD pipelines (Azure Dev Ops/Jenkins/Git Lab) 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 Auto Sys/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 Dev Ops/Jenkins/Git Lab; strong Git practices and code‑review hygiene.
Co…
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