Location: Dallas, TX
Rate: $60/hr on Corp to Corp
Duration: Long term
Mode of interview: One Virtual and One face to face
Mode of Job: 5 Days onsite
Data Engineer Quality Engineer
We are seeking a Data Engineer Quality Engineer to ensure the reliability, accuracy,
performance, and scalability of our data platforms and pipelines. This role focuses on
validating end-to-end data ingestion, processing, streaming, and analytics
workflows built on AWS, Kafka, SQL, Data Bricks and Python.
You will work closely with Data Engineers, Platform Engineers, Analytics teams, and
DevOps to embed quality into the data lifecycle ensuring trusted data, resilient pipelines,
and production ready systems.
Key Responsibilities
Validate batch and streaming data pipelines for correctness, completeness,
consistency, and timeliness.
Create and maintain data quality checks (nulls, duplicates, schema drift, referential
integrity).
Verify business rules and transformations using SQL based validations.
Design, develop, and execute test strategies for Databricks-based data pipelines
and analytics workflows
Establish data reconciliation and endtoend traceability between source and
downstream systems.
Test ETL/ELT pipelines built using AWS services (Glue, Lambda, EMR, Step
Functions).
Validate transformations written in SQL and Python.
Ensure correctness across data ingestion, enrichment, aggregation, and
publishing layers.
Test reprocessing, backfills, and historical data loads.
Validate ETL/ELT processes built using Apache Spark (PySpark/Scala) in
Databricks
Validate Kafka-based streaming pipelines for data integrity, ordering, and
exactlyonce /atleastonce semantics.
Test producer and consumer logic, serialization formats (Avro, JSON, Protobuf).
Validate topic configurations, partitions, offsets, retention policies, and schema
changes.
Simulate and test late arrivals, duplicate events, and consumer failures.
Test data workflows using AWS S3, Glue, Lambda, Redshift, Athena, Kinesis,
DynamoDB, or similar services.
Validate IAM roles, permissions, and secure data access.
Verify data lifecycle policies, encryption, and storage optimizations.
Build and maintain automated data testing frameworks using Python.
Develop reusable test utilities, fixtures, and synthetic datasets.
Integrate data tests into CI/CD pipelines for premerge, scheduled, and
postdeployment validation.
Enable automated alerts for data quality failures.
Validate pipeline performance for largescale datasets.
Test throughput, latency, and concurrency under peak workloads.
Validate retry logic, error handling, idempotency, and recovery mechanisms.
Perform soak, regression, and failover testing
Validate data pipeline metrics, logs, and alerts using CloudWatch, Prometheus,
Grafana, or equivalent tools.
Partner with teams to define data SLAs and SLOs.
Participate in incident response, rootcause analysis, and postmortems related to data
quality issues
Required Qualifications
7+ years of experience in QA, SDET, or Data Quality Engineering roles.
Strong hands-on experience with SQL for complex data validation and analysis.
Proficiency in Python for test automation and data validation.
Experience testing data pipelines and ETL/ELT workflows.
Handson experience with Kafka or other streaming platforms.
Solid understanding of AWS data services (S3, Glue, Redshift, Lambda, Athena,
etc.).
Experience working with large datasets and distributed systems.
Strong debugging, analytical, and problem solving skills.
