Full-Stack Engineer – Automation & AI Evaluation at Streamhub in Bengaluru, IN

Job description

• This is a single, combined engineering role: you build product and you own the automated systems that prove it works.
• Concretely, expect roughly 60% feature engineering — backend services, APIs, data models, and React front-end work on our customer-facing analytics platform — and 40% quality engineering, where you own the automation frameworks, data validation, and CI quality gates that keep a high-volume measurement platform trustworthy.
• This is not a manual QA position, and it is not a test-automation-only position. We are looking for an engineer who writes production code and believes automation is the default answer to quality at scale. You will sit in a product squad, ship to customers, and be the person who makes sure nobody downstream has to manually check whether the numbers are right.

Responsibilities

• Build
• 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.
• Assure
• 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.
• Apply AI
• 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

• Engineering
• 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.
• Quality & Automation
• You 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.
• You can 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 — you treat 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 — you isolate whether a failure is in the test, the code, the data, or the environment, and you close the loop with an automated check that catches it next time.
• Mindset
• Fluency with AI-assisted development tools, and the judgement to know when to trust their output.
• An ownership mindset: you scope ambiguous problems, decide what’s worth automating, and follow work through to release.
• 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.

Skills

Required

• Python
• JavaScript/TypeScript
• React
• Advanced SQL
• API automation
• Test automation framework design
• CI/CD integration
• Data validation and reconciliation

Preferred

• LLM/agent-based feature testing
• Snowflake, dbt
• Distributed data systems
• Event-driven architectures
• Adtech/media measurement
• Java
• 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.