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Data Engineer(AWS)

Plugins IncDallas, TX🇺🇸United States

Why this role stands out

This role offers a fantastic opportunity to elevate your skills in data quality and validation within a dynamic technology environment, working with cutting-edge AWS, Kafka, and Databricks technologies. You'll thrive here if you're a mid-senior level professional passionate about ensuring data integrity and building robust data systems, and you're eager to collaborate with talented engineering teams. Apply now to make a significant impact on data reliability and drive high-quality data solutions.

Quick overview

SeniorityMid Senior
Work modeOn Site
LocationDallas, TX, United States
DynamoDBSQLScalaApache SparkAWSETLEncryptionDatabricksGrafanaKafkaPrometheusPythonRedshiftCI/CDIAM

Job description

Job Title: Data Quality Engineer
Location: Dallas, TX (Hybrid – 3 days onsite)
Job Type: Long-term Contract
Work Authorization: Open - W2 opportunity
Interview Process: In-person (Client interview- Mandatory)
We are looking for a Data Engineer Quality Engineer to drive quality across large-scale data platforms and pipelines.
This role focuses on validating batch and streaming data workflows built on AWS, Kafka, Databricks, SQL, and Python.
You will work closely with data engineering, platform, and analytics teams to ensure reliable, accurate, and production-ready data systems.
Key Responsibilities
Data Quality & Validation
* Validate batch and streaming pipelines for correctness, completeness, and timeliness
* Implement data quality checks (nulls, duplicates, schema drift, referential integrity)
* Perform SQL-based validation of business rules and transformations
* Ensure end-to-end data reconciliation and traceability
ETL / ELT Pipeline Testing
* Test pipelines built using AWS services (Glue, Lambda, EMR, Step Functions)
* Validate transformations written in SQL and Python
* Test ingestion, enrichment, aggregation, and publishing layers
* Validate backfills, reprocessing, and historical data loads
* Test Spark-based pipelines (PySpark/Scala) in Databricks
Streaming & Kafka Testing
* Validate Kafka-based pipelines for data integrity, ordering, and delivery semantics
* Test producers/consumers and serialization formats (Avro, JSON, Protobuf)
* Validate topics, partitions, offsets, retention policies, and schema evolution
* Simulate edge cases such as late arrivals, duplicates, and consumer failures
AWS Data Platform Validation
* Work with S3, Glue, Lambda, Redshift, Athena, Kinesis, DynamoDB
* Validate IAM roles, permissions, and secure data access
* Verify data lifecycle policies, encryption, and storage optimization
Automation & Frameworks
* Build and maintain automated data testing frameworks using Python
* Develop reusable test utilities and synthetic datasets
* Integrate testing into CI/CD pipelines
* Enable automated alerts for data quality issues
Performance & Reliability Testing
* Validate pipeline performance for large datasets (throughput, latency, concurrency)
* Test retry logic, error handling, idempotency, and recovery mechanisms
* Perform regression, soak, and failover testing
Monitoring & Observability
* Validate metrics, logs, and alerts using CloudWatch, Prometheus, Grafana
* Define and support data SLAs/SLOs
* Participate in incident response and root cause analysis
Required Qualifications
* 7+ years of experience in QA, SDET, or Data Quality Engineering
* 3+ years of hands-on experience with Databricks
* Strong SQL skills for complex data validation
* Proficiency in Python for automation and data testing
* Experience testing ETL/ELT pipelines
* Hands-on experience with Kafka or other streaming platforms
* Solid understanding of AWS data services (S3, Glue, Redshift, Lambda, Athena)
* Experience working with large-scale distributed systems
* Strong analytical, debugging, and problem-solving skills
Preferred Skills
* Experience with Spark (PySpark/Scala)
* Knowledge of CI/CD integration for data testing
* Familiarity with data observability tools and frameworks

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