Role type Backend / Data-correctness QA Engineer (not purely UI/manual)

Seniority Mid-level

Experience 35 years (with 12 years testing backend/data systems) Employment type Full-time

Location Bengaluru / Mumbai

Work mode On-site

Reports to Engineering Manager, SixthSense

Team SixthSense IoT Data Platform

Domain IoT telemetry, energy/solar, streaming data pipelines

About Alphabin

Alphabin is a software quality engineering company that partners with product teams to ship reliable, correct software at scale. We build and run QA for complex backend and data platforms API and automation frameworks, data-pipeline validation, and CI/CD-integrated regression — so our clients can deploy faster with confidence. Our engineers work close to real production systems and own quality end to end, not just surface-level UI checks.

About the engagement

You'll work on a large-scale IoT data platform that ingests real-time telemetry from field devices across dozens of OEM vendors, computes business-critical KPIs (generation, loss, deemed energy, liability), and serves both customer-facing reporting and internal operations. The backend spans a Java data pipeline, NestJS services, and a Go ingestion layer, processing continuous streams from live assets in the field.

Why this role exists

The platform ingests real-time telemetry from devices across multiple OEMs, computes KPIs, and surfaces them to internal ops and customers. Bugs here are silent and expensive — they show up as wrong business numbers or missing data weeks later, not as a crash. We need a QA engineer who tests data correctness, not just UI flows.

What you'll do

- Own end-to-end QA for the ingestion processing KPI reporting pipeline (MQTT/OEM REST Kafka MongoDB/Postgres/ClickHouse APIs reporting UI).
- Design and run test cases for numeric correctness: deemed vs. real values, loss attribution, liability formulas, offline/never-connected asset handling, backfill jobs.
- Validate new OEM adapter onboarding — confirm field mapping, sign conventions,



and edge cases (partial data, device offline, malformed payloads) before go-live.
- Test Kafka-based ingestion paths including DLQ behavior — verify failed/malformed messages are captured with correct identifiers rather than silently dropped.
- Run API-level regression against staging using Postman/automated suites; maintain and extend collections.
- Query MongoDB/Postgres directly to independently verify pipeline outputs against raw source data, not just trust the API response.
- Write regression suites that catch pipeline-breaking changes before production across a multi-repo, multi-language backend (NestJS, Java, Go).
- Triage production data anomalies — reproduce, isolate root cause to a specific service/job, and file a precise bug report engineers can act on immediately.
- Participate in release sign-off across the data pipeline and API repos per the team's branching/QA policy.
- Must-have skills

- 3–5 years QA experience, with at least 1–2 years testing backend/data systems (not purely UI/manual).
- Comfortable writing and running basic SQL (Postgres) to verify data independently.
- API testing fluency — Postman, REST semantics, auth (Bearer tokens), reading OpenAPI/Swagger specs.
- Understands eventual consistency / async pipelines — knows how to test where "the fix is deployed" and "the data is correct" can be hours apart (batch jobs, schedulers, backfills).
- Strong bug-report discipline: root cause hypothesis, exact repro steps, affected data range — not "it's broken."
- Basic scripting (Python or Node) for throwaway verification scripts against APIs/DBs.
- Automation:



experience creating frameworks from scratch (Playwright, Selenium, REST Assured, Appium).
- Build, maintain, and scale automated test suites (UI and API) for regression and core funnels.
- Use AI coding assistants to write, optimize, and maintain test automation scripts faster.
- Integrate automated tests into the CI/CD pipeline to catch issues before release.

Good to have

- Prior experience testing IoT/telemetry data — device-level streams (MQTT, dataloggers, sensor payloads), device offline/reconnect states, timestamp/timezone correctness, or validation against hardware ground truth. Robust differentiator for this role.
- Exposure to any Kafka-based or event-streaming pipeline.
- Familiarity with ClickHouse or other time-series databases.
- Experience testing multi-tenant B2B/B2C data.
- Basic Java or Go reading ability (to understand pipeline code, not to write features).
- Energy, solar, or utilities domain background.
- What "great" looks like in the first 90 days

- Independently reproduces a reported data discrepancy by tracing it from the reporting UI back to the raw MongoDB/Kafka event, without engineering hand-holding.
- Has built a regression checklist for OEM adapter onboarding that the team reuses for every new vendor.
- AI tool proficiency: hands-on use of AI tools (ChatGPT, Claude, Copilot, testRigor, or similar) in daily QA workflow for test writing, script generation, and bug analysis.
- Catches at least one KPI/liability calculation bug via direct data query before it reaches a customer report.
- Why join Alphabin

- Work on deep, technically demanding QA — data pipelines and real production systems, not click-through scripts.
- Build automation frameworks from the ground up and own quality end to end.
- Grow across multiple languages and stacks (NestJS, Java, Go) and modern data infrastructure (Kafka, ClickHouse).
- A team that treats QA as engineering, with AI-assisted workflows built into how we work.

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