About the Role
We are looking for a Data Assurance Engineer with strong experience in data validation, automation, analytics testing, and large-scale event pipeline quality.
This role is focused on ensuring the accuracy, stability, and reliability of data across our analytics and security event systems. The ideal candidate will be responsible for building automated validations, investigating data discrepancies, monitoring data quality, and working closely with engineering, data, and product teams.
Responsibilities
Design, build, and maintain automated validation processes for large-scale event pipelines.
Validate end-to-end data flows across ingestion, processing, storage, and dashboard layers.
Create SQL-based validations to verify event counts, unique devices, metadata accuracy, schema
consistency, latency, and data freshness.
Investigate discrepancies between production systems, staging environments, data warehouses, object
storage, and customer-facing dashboards.
Monitor event volume, data latency, anomalies, spikes, drops, duplicates, and missing data.
Build and maintain CI/CD validation jobs using tools such as Jenkins or GitLab CI.
Create clear automated reports, dashboards, and email summaries for validation results.
Work closely with backend engineers, data engineers, QA teams, and product stakeholders to identify,
report, and validate fixes for data quality issues.
Support performance and scalability testing for analytics dashboards, queries, and data pipelines.
Help improve internal data assurance processes, data observability, and production monitoring.
Requirements
2+ years of experience in Data QA, Data Validation, QA Engineering, or a similar role.
Strong hands-on experience with SQL and data validation.
Experience testing or validating analytics systems, event pipelines, ETL/ELT processes, or high-volume data platforms.
Experience with automation using JavaScript/Node.js, Python, or another programming language.
Ability to investigate complex data issues across multiple systems.
Good understanding of APIs, logs, databases, object storage, and data processing flows.
Experience creating automated reports or validation summaries.
Strong analytical thinking, attention to detail, and ownership mindset.
Advantages
Experience with ClickHouse, Athena, S3, Kafka, Metabase, or similar technologies.
Experience with Playwright or other automation frameworks.
Experience validating Parquet files, schema consistency, and large-scale event data.
Experience with security analytics, event pipelines, device identifiers, or customer-facing analytics dashboards.
Experience with CI/CD tools such as Jenkins, GitLab CI, or similar.
Experience with Docker and AWS services.