Data Quality Engineer (AWS, Airflow, DBT - Max $70/hr W2 )

Lenmar Consulting Inc

Reston, United States of America

yesterday

Role details

Contract type

Permanent contract

Employment type

Full-time (> 32 hours)

Working hours

Regular working hours

Languages

English

Job location

Reston, United States of America

Tech stack

Airflow

Amazon Web Services (AWS)

Automation of Tests

Code Coverage

Data as a Services

Data Validation

Data Cleansing

Information Engineering

ETL

Data Transformation

Data Migration

Data Warehousing

DevOps

Python

DataOps

Test Data

Strategies of Testing

Data Ingestion

Data Build Tool (dbt)

Test Scripts

Data Lake

PySpark

Amazon Web Services (AWS)

Data Management

Data Pipelines

Serverless Computing

Redshift

Job description

Data Quality Engineer (AWS Data Platform) Overview We are seeking a highly skilled Data Engineering - Quality Engineer to define and implement end-to-end testing strategies for a modern data platform built on AWS. This role will be responsible for ensuring data quality, reliability, and performance across the entire pipeline; from ingestion to transformation and reporting., * Define the end-to-end testing scope based on solution architecture and project documentation

Design and implement a comprehensive testing strategy and plan aligned with organizational QA standards
Develop and maintain test scripts and frameworks for the Redshift serverless platform
Perform testing across key technologies, including:

AWS Redshift
AWS DMS (Data Migration Service)
AWS Glue
PySpark Deequ
Event Bridge
Data Lakes
Python-based data pipelines
Apache Airflow
dbt (data build tool)

Build and implement automated testing solutions to ensure:

End-to-end data validation
Data ingestion accuracy
Transformation logic integrity
Data pipeline reliability

Conduct test coverage analysis and ensure adequate validation across all data engineering workflows
Prepare and manage test data
Review and provide feedback on:

Solution architecture
Data models
Design and technical documentation

Collaborate with cross-functional teams (Data Engineering, BI, DevOps, Product) to:

Identify testing impacts
Mitigate risks
Ensure high-quality deliverables

Requirements

Proven experience in data engineering testing / data QA / ETL validation
Strong hands-on experience with AWS data services (Redshift, Glue, DMS)
Proficiency in Python for test automation and validation
Experience with Airflow and orchestration testing
Hands-on experience with dbt and data transformation validation
Familiarity with CDK for infrastructure validation
Experience in BI testing in Quicksuite will be highly beneficial
Experience with data quality tools such as PySpark Deequ or similar
Strong understanding of: Data warehousing concepts, ETL/ELT pipelines. Data validation techniques (schema, reconciliation, anomaly detection)

Preferred Qualifications