Algoworks

Senior Quality Assurance Engineer

Noida, India
SQL ETL API Python Docker Kubernetes
Description

Senior Level Quality Assurance Engineer

Location: Noida, India

Department: Product Engineering - Cloud and Digital

Experience: 8+

Skills: ETL, SQL

Role Summary

We are seeking a highly technical, data-focused Senior QA Automation Engineer to support Corporate Technology initiatives across accounting, treasury, finance, and enterprise integration platforms. This role will be central to validating complex data flows involving Airflow and related financial-system integrations.
This role is best suited for a QA engineer who is strong in integration testing, data validation, reconciliation, and API- and file-based workflows, with experience using AI-assisted automation. There will be little to no traditional UI testing. Success in this role depends on the ability to understand complex system flows quickly, ask pointed questions early, build automation alongside testing, and communicate clearly across engineering, product, finance, treasury, and vendor teams.

Key Responsibilities

    • Own QA strategy and execution for enterprise integration initiatives, including Airflow implementation.
    • Design and execute tests for API, ETL, batch, and file-based workflows across upstream and downstream systems.
    • Build automated validation assets as testing progresses, including SQL checks, reconciliation queries, Python-based data-validation scripts, API test suites, and reusable regression utilities.
    • Use AI coding assistants (e.g., Codex, Claude Code) to accelerate automation, data-analysis utilities, test scaffolding, and repeatable QA assets, while validating all generated output for correctness and maintainability.
    • Validate source-to-target mappings, transformations, business rules, ledger impacts, exception handling, duplicate prevention, rerun safety, and operational controls.
    • Test Airflow DAG behavior, including scheduling, dependencies, retries, failure handling, backfills, parameterization, and reruns.
    • Partner with business and technical stakeholders to clarify requirements, identify gaps, and proactively surface risks before they become production issues.
    • Support release readiness, cutover validation, post-deployment verification, and production-issue triage.

Required Qualifications

    • 6+ years of QA experience, with a strong emphasis on integration, ETL, data, financial-systems, or enterprise-platform testing.
    • Strong SQL skills and experience validating data transformations, reconciliations, balancing, completeness, and accuracy.
    • Hands-on experience testing APIs, file-based integrations, and batch or workflow-driven processes.
    • Strong automation mindset, with experience creating scripts, utilities, or frameworks to automate testing and validation as part of day-to-day QA execution.
    • Demonstrated ability to use AI-assisted coding tools (e.g., Codex, Claude Code) to accelerate automation and technical QA work.
    • Ability to read, review, and validate code or scripts, especially in Python (or similar languages).
    • Strong written and verbal communication skills.
    • Proactive working style; does not wait to be told every next step.
    • Comfortable asking questions and challenging unclear assumptions early.
    • Quick learner who can ramp up rapidly on new systems, workflows, and business processes.

Preferred Qualifications

    • Experience with Airflow or similar workflow-orchestration tools.
    • Experience with accounting, treasury, capital markets, bank files, ERP, or general-ledger systems.
    • Familiarity with containers, logging, CI/CD pipelines, SonarQube, Docker, and/or Kubernetes.
    • Experience supporting vendor implementations or complex enterprise system upgrades.
    • Experience in mortgage, lending, fintech, or financial-services environments.

What Success Looks Like

    • Builds automation while testing—not after the fact.
    • Catches integration, reconciliation, and control issues early.
    • Uses AI tools to move faster, but never blindly trusts generated code or test logic.
    • Communicates risks clearly and drives alignment amid ambiguous requirements.
    • Becomes a force multiplier for both testing speed and test depth.

Algoworks
Algoworks

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