QA Engineer – Data Ingestion & Data Platform

We are seeking a hands-on QA Engineer with strong experience in test automation, data validation, API testing, ETL/data pipeline testing, and cloud-based applications. The QA Engineer will be responsible for establishing and executing comprehensive quality strategies across data ingestion pipelines, APIs, applications, integrations, and downstream data platforms.

The ideal candidate will combine strong traditional software testing expertise with data-centric QA skills and experience working with modern technologies such as AWS, Snowflake, Python, SQL, REST APIs, and automated testing frameworks.

This role is critical to ensuring that data entering the Geode ecosystem is accurate, complete, timely, consistent, traceable, and fit for downstream investment and analytical use.

Key Responsibilities

QA Strategy & Test Planning

• Develop comprehensive QA strategies for data ingestion applications, APIs, services, and data pipelines.
• Translate business and technical requirements into detailed test plans, test cases, and acceptance criteria.
• Define functional, integration, regression, performance, security, and data-quality testing approaches.
• Establish reusable testing standards, frameworks, and best practices.
• Work closely with developers, data engineers, architects, business analysts, and project managers throughout the SDLC.
• Identify potential quality risks early in the development lifecycle.
• Participate in Agile ceremonies, requirements reviews, design reviews, and sprint planning.

Data Ingestion & Data Quality Testing

• Test end-to-end data ingestion pipelines from source systems through processing, transformation, storage, and downstream consumption.
• Validate data completeness, accuracy, consistency, timeliness, and integrity.
• Perform source-to-target data reconciliation.
• Validate data transformations, mappings, calculations, business rules, and enrichment processes.
• Test structured and semi-structured data including CSV, JSON, XML, database extracts, and API-based data.
• Validate data ingestion error handling, rejected records, retries, exception processing, and recovery.
• Develop automated data-quality validation and reconciliation processes.
• Test batch and event-driven ingestion workflows.
• Validate data lineage and traceability where applicable.
• Identify and investigate data anomalies and discrepancies.

SQL & Database Testing

• Develop complex SQL queries to validate data across source, staging, transformation, and target environments.
• Perform source-to-target reconciliation and data comparison.
• Validate database schemas, tables, views, stored procedures, and data transformations.
• Perform data profiling and identify unexpected data patterns.
• Validate large data volumes and complex relational data structures.
• Test data migration and data transformation processes.
• Work with Snowflake and other cloud-based data platforms.

API & Integration Testing

• Develop and execute automated tests for REST APIs and web services.
• Validate API requests, responses, authentication, authorization, error handling, and performance.
• Test integrations between ingestion services, applications, databases, cloud services, and external data providers.
• Validate JSON/XML payloads and data contracts.
• Test API failure scenarios, retries, timeouts, and recovery mechanisms.
• Use tools such as Postman, REST Assured, SoapUI, or comparable technologies.

Test Automation

• Build and maintain automated test frameworks for application, API, and data testing.
• Develop automated regression suites to reduce manual testing effort.
• Integrate automated tests into CI/CD pipelines.
• Implement automated validation at multiple stages of the data ingestion lifecycle.
• Create reusable testing utilities, libraries, and test data frameworks.
• Maintain automated tests as applications and data pipelines evolve.
• Analyze automated test results and identify defects and quality trends.

Preferred Automation Technologies

• Python
• Java
• Selenium
• Playwright
• Cypress
• PyTest
• JUnit / TestNG
• REST Assured
• Postman
• Great Expectations or comparable data-quality frameworks

Cloud & Data Platform Testing

Experience testing applications and data workloads deployed within AWS is highly desirable.

The QA Engineer should be comfortable testing environments involving:

• Amazon S3
• AWS Lambda
• AWS Glue
• Amazon ECS/EKS
• Amazon RDS
• Amazon Redshift
• AWS Step Functions
• AWS CloudWatch
• Kafka / Amazon MSK
• Snowflake
• REST APIs
• Cloud-based data pipelines

The candidate should understand how to validate data and application behavior across distributed cloud environments.

Performance & Reliability Testing

• Develop and execute performance and load tests for APIs, applications, and data ingestion services.
• Validate system performance under expected and peak data volumes.
• Identify bottlenecks across applications, APIs, databases, and data pipelines.
• Test batch processing windows and ingestion throughput.
• Validate scalability and response times.
• Support capacity and performance benchmarking.
• Test failure recovery, retry mechanisms, and system resilience.

Experience with tools such as JMeter, LoadRunner, Gatling, or similar technologies is preferred.

Defect Management & Production Quality

• Identify, document, prioritize, and track defects through resolution.
• Perform root-cause analysis in collaboration with developers and data engineers.
• Validate defect fixes and perform regression testing.
• Monitor recurring defects and identify systemic quality issues.
• Support UAT and production validation.
• Participate in production incident investigations where data or application quality is involved.
• Develop quality metrics and reporting for project leadership.

Financial Services & Data Controls

Experience in financial services, asset management, investment management, or capital markets is strongly preferred.

The QA Engineer should understand the importance of:

• Data accuracy and completeness
• Data lineage and traceability
• Data reconciliation
• Auditability
• Security and access controls
• Data timeliness
• Exception management
• Production controls
• Regulatory and compliance requirements

Experience testing investment-related data such as positions, transactions, securities, portfolios, benchmarks, market data, performance, reference data, or pricing data is a significant advantage.

Required Qualifications

• 10+ years of software QA/testing experience.
• Strong hands-on experience with automated testing.
• Strong SQL skills and experience testing data-intensive applications.
• Experience testing ETL/ELT and data ingestion pipelines.
• Strong API testing experience.
• Experience with Python, Java, or another programming language.
• Experience with automated testing frameworks such as Selenium, Playwright, Cypress, PyTest, JUnit, or TestNG.
• Experience testing REST APIs and microservices.
• Experience with CI/CD environments and integrating automated testing into deployment pipelines.
• Experience working with cloud-based applications, preferably AWS.
• Ability to work independently while collaborating closely with developers, data engineers, architects, and business stakeholders.

Ideal Candidate

The ideal candidate is a hands-on QA Engineer who combines strong software-testing expertise with deep data and automation skills.

This person should be comfortable testing across the entire technology stack—from source systems and APIs through ingestion pipelines, AWS infrastructure, application services, Snowflake, and downstream data consumers.

The strongest candidates will have a combination of Python, SQL, API testing, automated testing, data validation, AWS, Snowflake, and data-pipeline testing experience, preferably within a financial-services or investment-management environment.

The candidate should have a strong quality mindset and understand that for an investment-management organization, high-quality data is foundational to portfolio management, analytics, reporting, risk management, and business decision-making.