QA Engineer – AWS Data Platform

DICETEK LLC; Abu Dhabi; On-site; AED 420,000 - 660,000; Full time

Job summary

DICETEK LLC is seeking a QA/Data Testing professional to validate enterprise data platforms and data pipelines. The role emphasizes hands-on testing across data lakes and cloud warehouses, with strong SQL, automation, and AWS experience.

You’ll work in an agile setting, ensuring data quality, governance, and secure handling of test data, while collaborating with stakeholders. The ideal candidate brings 5+ years in data QA, understands ETL/ELT, and can translate technical findings into business

Qualifications

• Experience testing enterprise-scale data platforms and data lakes.
• Proficient in SQL for complex data reconciliation and profiling.
• Automation using Python and PySpark for data validation.
• Hands-on with AWS data services: Glue, S3, Redshift, Athena, Lake Formation.
• Understanding of ETL/ELT, pipelines, metadata and lineage.
• Experience validating Iceberg or similar formats and data consistency.
• Strong data-quality knowledge across dimensions and controls.
• Familiar with pytest, Great Expectations, Deequ or similar tools.
• Testing data integrations across databases, APIs, files and streaming sources.
• Ability to plan tests, manage risks, defects, and releases; knowledge of IAM & encryption.
• Experience with GitHub/GitHub Actions or AWS CodePipeline; CI/CD.
• Agile environment experience with JIRA/Confluence and release reporting.
• Analytical mindset and clear stakeholder communication.

Skills

Data platform testing

SQL proficiency

Python automation

AWS data services

ETL/ELT knowledge

Iceberg/table formats

Data quality controls

Test frameworks

Data integration testing

Test planning & QA lifecycle

IAM & data security

Version control & CI/CD

Agile with JIRA/Confluence

Analytical & stakeholder skills

Education

Quality Assurance / Data Testing background

Experience in Banking / Financial Services

Regulatory & data governance understanding

Full testing lifecycle expertise

Job description

Job Description

Years of Experience *

Minimum 5 years

Required Skills *

Primary technical and functional skills

• Strong hands-on experience testing enterprise-scale data platforms, data lakes, lakehouses or cloud data warehouses.
• Advanced SQL skills for complex reconciliation, profiling, transformation validation and root-cause analysis.
• Strong automation experience using Python and preferably PySpark for high-volume data validation and regression testing.
• Practical experience testing AWS data services including AWS Glue, Amazon S3, Amazon Redshift, Amazon Athena, AWS Lake Formation and Glue Catalog.
• Strong understanding of ETL/ELT, batch and streaming pipelines, data orchestration, metadata, lineage and schema management.
• Experience validating Apache Iceberg or similar open table formats, including schema and partition evolution, snapshots and data consistency.
• Strong knowledge of data-quality dimensions and controls, including completeness, accuracy, validity, consistency, uniqueness, timeliness and referential integrity.
• Experience with automated test frameworks such as pytest, Great Expectations, Deequ or equivalent data-quality tooling.
• Experience testing data integrations across relational databases, APIs, files/object storage and streaming or event-based sources.
• Experience with test planning, requirements traceability, risk-based testing, defect management, regression testing and release assurance.
• Good understanding of IAM, data-access controls, masking, encryption and secure handling of test data within AWS.
• Experience with version control and CI/CD using GitHub and Jenkins, GitHub Actions or AWS CodePipeline.
• Experience working in Agile environments using JIRA and Confluence, with evidence-based quality reporting and clear release recommendations.
• Strong analytical, troubleshooting and stakeholder-communication skills.

Required Experience / Knowledge Areas *

• Strong professional experience in Quality Assurance, Data Testing or Test Automation for Enterprise Data Platforms.
• Demonstrable experience assuring production-grade data pipelines and analytical datasets in complex enterprise environments.
• Experience working within Banking or Financial Services is strongly preferred.
• Understanding of data governance, security, privacy, auditability and regulatory expectations in a controlled enterprise environment.
• Experience across the full testing lifecycle, from requirements and source-data analysis through test design, execution, defect resolution, deployment validation and production support.
• Ability to challenge unclear requirements, identify data risks early and translate technical findings into clear business impact.