Staff QE

AlphaSense

7d•$148,350 - $203,550

About The Position

As the Staff Quality Engineer dedicated to REDS, you will define how we prove our data is correct before it reaches customers. You will set the strategy, build the practice from the ground up, and work closely with engineering and product leadership to make quality a measurable part of how the team ships.

Requirements

8+ years of hands-on experience in quality engineering, including setting strategy for complex backend or data platforms.
Hands-on experience designing and automating tests for backend and data systems, including API and contract testing, data pipeline validation, and event-driven or streaming interfaces (e.g., Kafka).
Strong skills in at least one programming language (such as Python, Kotlin/Java, TypeScript/JavaScript, or Ruby), and comfort working in unfamiliar codebases.
Deep expertise in test design across unit, API, and end-to-end layers, and judgment about where each belongs.
Experience validating data correctness at scale, ideally in matching, identity, or pipeline-driven systems.
A strong grasp of continuous delivery, CI/CD, and release quality gates.
A track record of using AI to speed up engineering and testing.
Proven ability to set direction on your own and influence without authority.
Clear, confident communication with technical and business stakeholders.

Nice To Haves

Experience with data pipelines or data-operations tooling.
Hands-on E2E automation experience with Playwright
Experience with financial, company, or reference data.
Experience evaluating AI or LLM output.
BS/MS in a relevant technical field.

Responsibilities

Own the quality strategy for AlphaSense's company reference data, built on quality by design and automation by default.
Close test coverage gaps on our critical workflows: ingesting provider data, matching records, keeping identifiers stable over time, and delivering entity data and change events to the product teams that rely on them.
Define what "correct" means for company data, and build the golden datasets, labeled samples, and automated checks that prove it, including measuring match precision and recall and the tradeoff between false merges and false splits.
Set the quality measures and release standards the team uses to ship with confidence.
Build and grow scalable automation across our data, API, and event-driven interfaces, and decide what belongs at the unit, component, API, and E2E layers.
Anticipate data lifecycle risks such as provider data drift, schema changes, and late or conflicting records, and build regression coverage for them.
Partner with developers on test planning for new features, so automation is ready as the code is.
Use production signals to focus effort where it reduces the most risk to customers.
Bring AI into how the team designs, maintains, and triages tests.
Coach engineers and influence across teams to raise the quality bar.