Posted today · be early
Full-Stack Engineer – Automation & AI Evaluation
Streamhub Limited
IndiaremotePosted today
Skill Required
Full-Stack-EngineerSoftware-EngineerAutomation-EngineerQuality-EngineerAutomation-TestingSenior-Full-Stack-Engineer-(Automation)Senior-Full-Stack-AI-EngineerSenior-Full-Stack-AI-DeveloperFull Stack DeveloperAI EngineerautomationAIJMeterTest AutomationManual TestingData WarehousingTypeScriptEngineeringJavaScriptPlaywrightSnowflakesimilar)debugginganalyticsSeleniumbuildingCypresswrittenTestNGPythondesignScalaReactCI/CDRustFulltime
Key highlights
- Salary benchmarked against full-stack engineering bands
- 4+ years building software
- Flexible timing
- Combined feature and quality engineering role
Role overview
Streamhub is a data analytics and activation SaaS platform encompassing audience measurement, segmentation, and targeting. The company is in an exciting growth phase, making key hires for teams in India and Japan to build an actionable data platform for the video business. This is a combined engineering role involving roughly 60% feature engineering (backend services, APIs, data models, and React front-end) and 40% quality engineering (automation frameworks, data validation, and CI quality gates). The role is designed for an engineer who writes production code and believes automation is the default answer to quality at scale, sitting within a product squad to ship features and ensure data trustworthiness.
Responsibilities
- Design and ship backend services and APIs (Python and/or Node/TypeScript) against large-scale data systems.
- Contribute to data modelling and transformation logic over our Snowflake-based platform.
- Build and extend React front-end modules for customer-facing analytics.
- Take features end to end: design, implementation, tests, review, release, and post-release verification.
- Own and evolve API and web automation frameworks (Playwright, Cypress, Selenium, or similar).
- Design automated validation for ETL pipelines — reconciliation, schema and contract checks, aggregation and business-rule verification across high-volume datasets.
- Write advanced SQL for data integrity and reconciliation testing.
- Integrate quality gates into CI/CD so regressions, schema breaks, and contract violations are caught before deployment, not after.
- Investigate data quality issues end to end: scope the problem, fix the cause, and automate the check that stops it recurring.
- Use AI-assisted development tools (Claude Code, Cursor, Copilot or similar) as part of your normal workflow for implementation, test generation, and coverage expansion.
- Build evaluation harnesses for AI and agent-based features, where outputs are probabilistic and traditional assert-equals testing does not apply.
- Apply AI to anomaly, regression, and data-drift detection, and to root-cause analysis.
Requirements
- 4+ years building software, with demonstrable backend development experience — services and APIs you designed and shipped, not only test code.
- Strong hands-on coding in Python or JavaScript/TypeScript, to a standard where your production code and your test code are held to the same bar.
- Working front-end capability in React (you do not need to be a specialist).
- Advanced SQL and comfort working with large datasets, warehouses, and ETL pipelines.
- Have built or substantially owned a test automation framework from scratch — not just written tests inside someone else’s — including the design decisions: structure, fixtures, test data management, parallelisation, and reporting.
- Hands-on depth in API automation (contract, integration, and end-to-end) and web UI automation with Playwright, Cypress, Selenium, or similar.
- Ability to define a test strategy: deciding what belongs in unit vs. integration vs. E2E, what should never be automated, and where the real risk in a release actually sits.
- Experience integrating automated suites into CI/CD as enforcing quality gates, with attention to runtime, stability, and signal — treating a flaky suite as a defect to be fixed, not tolerated.
- Experience validating data correctness at scale: reconciliation, aggregation and transformation checks, schema and contract validation over pipelines.
- Strong debugging and root-cause instincts — isolating whether a failure is in the test, the code, the data, or the environment, and closing the loop with an automated check that catches it next time.
- Fluency with AI-assisted development tools, and the judgement to know when to trust their output.
- An ownership mindset: scoping ambiguous problems, deciding what’s worth automating, and following work through to release.
Nice to have
- Experience testing or evaluating LLM/agent-based features.
- Performance testing with JMeter, Gatling, or k6.
- Snowflake, dbt, or comparable modern data-stack experience.
- Familiarity with distributed data systems, event-driven architectures, or adtech/media measurement.
- Additional languages such as Java or Scala.
Benefits
- Competitive salary, benchmarked against full-stack engineering bands rather than QA bands.
- Immense learning, exposure to niche data technologies, and handling TBs of data.
- Work with an open, diverse, and autonomous team.
- Entrepreneurial team culture.
- Flexible timing.
Additional details
- This is not a manual QA position, and it is not a test-automation-only position.
- It is not a manual testing or test-execution role.
- It is not a role where quality work is a stepping stone you leave behind — it is a permanent part of the remit.
- It is not siloed. You will not be handed finished features to test; you will be in the design conversation.
- We are committed to equal employment opportunities regardless of race, colour, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender, gender identity or expression, or veteran status. We are proud to be an equal opportunity workplace.
- Originally posted on Himalayas
