Sr. Testing / QA Lead

Owning quality for a high-scale,

low-latency streaming Customer Data Platform deployed on infrastructure we run

ourselves (on-prem, Kubernetes), not managed cloud services. The platform

ingests from thirty or more source systems at hundreds of thousands of events

per second, resolves customer identity, computes customer attributes in real

time, and emits governed signals to external destinations. You will build and

own the automation that proves this works: API and end-to-end test frameworks,

streaming and data-pipeline validation, latency and load testing against

explicit SLOs, and governance testing that proves consent, data-protection, and

tenant-isolation rules actually hold at runtime. Testing here is

evidence-producing, not exploratory only - test outcomes gate whether an

artifact is allowed to go live.

Requirements

• 6+ years

in software quality engineering, with the majority of your work in test

automation rather than manual testing

• Demonstrated

ownership of test automation frameworks you built or substantially

re-architected - not only writing test cases against someone else's framework

(core requirement)

• Ability to

write high-quality code in Python, Java, TypeScript, or equivalent languages,

including framework structure: base classes, shared utilities, configuration

handling, reporting, and test data management

• Strong API

test automation experience (REST Assured, Karate, pytest, Postman/Newman,

Playwright API, or similar) - test organization, authentication handling,

response and schema validation, and contract testing between services

• End-to-end

UI automation experience (Playwright, Cypress, Selenium, or similar) -

framework architecture, Page Object Model or equivalent, handling dynamic

elements, and parallel execution

• Demonstrated

ability to diagnose and eliminate flaky tests - proper waits, test isolation,

deterministic setup and teardown, and root-cause investigation rather than

blanket retries

• Performance

and load testing experience (JMeter, k6, Locust, Gatling, or similar) -

designing load scenarios, identifying bottlenecks, and validating throughput

and latency against stated targets

• Strong SQL

for data validation - joins, aggregations, window functions, and

source-versus-target reconciliation queries over large datasets

• Practical

experience testing data pipelines and ETL/streaming jobs - schema validation,

source-to-destination comparison at row and value level, completeness and

duplication checks, and sampling strategies for datasets too large to compare

in full

• Experience

testing event-driven systems and message queues (Kafka or equivalent) - event

and payload validation, ordering guarantees, delivery semantics (at-least-once

versus exactly-once-effective), idempotency, consumer lag, and offset behaviour

• Experience

testing stateful stream processing (Apache Flink or similar) - correctness

after restart from checkpoint, savepoint-based upgrades, state restoration,

late and out-of-order event handling, and behaviour under backpressure

• Ability to

validate end-to-end latency against an explicit SLO - measuring p95 and p99

across pipeline stages, and distinguishing in-scope platform processing from

excluded external calls such as third-party API round-trips and destination

acknowledgement

• Experience

integrating tests into CI/CD pipelines - trigger configuration, test stages,

artifact and report publication, and failure gates that block promotion

• Disciplined

test data management - setup and cleanup strategy, inter-test dependency

handling, and environment-specific data; experience working with synthetic,

generated, or masked datasets where production data cannot be used

• Ability to

produce structured, auditable test evidence: test results tied to the exact

artifact version and configuration under test, so that results are traceable

and cannot be silently reused after the artifact changes

• Experience

testing data governance and privacy controls - consent enforcement, data

classification and usage rules, tokenization and masking, and verifying that

raw sensitive identifiers do not appear in downstream topics, logs, or exports

• Experience

testing identity resolution or entity matching - deterministic and

probabilistic matching outcomes, merge and split behaviour, lifecycle state

transitions, and identifier reassignment scenarios

• Experience

testing multi-tenant systems - verifying tenant and workspace isolation, role-

and attribute-based access control, and absence of cross-tenant data leakage

• Experience

validating replay, backfill, and reconciliation - comparing recomputed values

against originally emitted values and confirming that reruns do not re-trigger

external side effects

• Experience

validating analytical or lakehouse data (Apache Paimon, Iceberg, Delta Lake, or

similar, queried through Trino, Spark, or equivalent) - comparing emitted

streams against materialized tables for completeness and correctness

• Experience

with failure-injection and resilience testing - node, broker, cache, or service

loss; verifying recovery path, data integrity after recovery, and absence of

duplicate or lost records

• Experience

testing on Kubernetes-deployed platforms - namespace-scoped environments,

Helm-based deployments, pod and job lifecycle, and access to logs and metrics

for diagnosis

• Working

familiarity with observability tooling for test diagnosis (Grafana, Prometheus,

OpenSearch or equivalent log search, distributed tracing)

• Experience

validating AI/ML or LLM outputs is an advantage - approaches to testing

non-deterministic responses, accuracy and regression measurement, and detection

of unsupported or fabricated output

• Rigorous

defect discipline - reproducible defect reports with evidence, clear severity

and triage judgement, and ownership of a maintained regression suite

• Domain

exposure to any of the following is an advantage: customer data platforms or

customer 360 systems, telecom or other high-volume transactional systems,

real-time systems with latency SLAs, AdTech or MarTech platforms, and data

analytics or dashboard systems

• Familiarity

using AI tools for development and debugging (Claude, Cursor, Codex)