Data Quality Engineer
Full Time • Hybrid - US
Benefits:
- W2 OPPORTUNITY
- Competitive salary
- Opportunity for advancement
Job Title: Data Quality Engineer (Databricks, Kafka, AWS)
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 Quality Engineer to own validation across batch and streaming data pipelines. This role focuses on ensuring data correctness, reliability, and performance across platforms built on Databricks, Kafka, AWS, SQL, and Python.
This is a hands-on role focused on building scalable data validation frameworks and ensuring production-grade data systems.
Key Responsibilities
End-to-End Data Validation
* Validate data pipelines for accuracy, completeness, consistency, and timeliness
* Build SQL-based validations for business rules and transformations
* Implement reconciliation between source and downstream systems
* Ensure data lineage and traceability
ETL / ELT & Spark Testing
* Test pipelines built on AWS (Glue, Lambda, EMR, Step Functions)
* Validate transformations using SQL and Python
* Test ingestion, transformation, aggregation, and serving layers
* Handle backfills, reprocessing, and historical data loads
* Validate Spark pipelines (PySpark/Scala) on Databricks
Streaming (Kafka)
* Validate data integrity, ordering, and delivery guarantees
* Test producer and consumer logic and serialization formats (Avro, JSON, Protobuf)
* Validate topics, partitions, offsets, retention, and schema evolution
* Simulate late events, duplicates, and failure scenarios
Automation & Frameworks
* Build Python-based data testing frameworks
* Develop reusable validation utilities and synthetic datasets
* Integrate data tests into CI/CD pipelines
* Enable automated alerts for data quality issues
Performance & Reliability
* Validate throughput, latency, and concurrency at scale
* Test retry logic, idempotency, and recovery mechanisms
* Perform regression, soak, and failover testing
Observability
* Validate logs, metrics, and alerts using tools such as CloudWatch, Prometheus, and Grafana
* Define and monitor data SLAs and SLOs
* Support incident response, root cause analysis, and postmortems
Required Qualifications & Experience
* 7+ years of total experience in QA, SDET, or Data Quality Engineering
* Minimum 4–6 years of hands-on experience working with data platforms, data pipelines, or data engineering ecosystems
* 3+ years of hands-on experience with Databricks and Apache Spark
* Strong SQL skills for data validation, reconciliation, and complex analysis
* Proficiency in Python for automation and data validation
* Experience testing ETL/ELT pipelines (batch and streaming)
* Hands-on experience with Kafka or similar streaming platforms
* Strong understanding of AWS data services (S3, Glue, Lambda, Redshift, Athena)
* Experience working with large-scale distributed data systems
* Strong debugging, analytical, and problem-solving skills
Nice to Have
* Experience with data quality or observability tools such as Great Expectations or Monte Carlo
* Knowledge of schema registry and data contracts
* Experience with CI/CD tools such as GitHub Actions or Jenkins
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 Quality Engineer to own validation across batch and streaming data pipelines. This role focuses on ensuring data correctness, reliability, and performance across platforms built on Databricks, Kafka, AWS, SQL, and Python.
This is a hands-on role focused on building scalable data validation frameworks and ensuring production-grade data systems.
Key Responsibilities
End-to-End Data Validation
* Validate data pipelines for accuracy, completeness, consistency, and timeliness
* Build SQL-based validations for business rules and transformations
* Implement reconciliation between source and downstream systems
* Ensure data lineage and traceability
ETL / ELT & Spark Testing
* Test pipelines built on AWS (Glue, Lambda, EMR, Step Functions)
* Validate transformations using SQL and Python
* Test ingestion, transformation, aggregation, and serving layers
* Handle backfills, reprocessing, and historical data loads
* Validate Spark pipelines (PySpark/Scala) on Databricks
Streaming (Kafka)
* Validate data integrity, ordering, and delivery guarantees
* Test producer and consumer logic and serialization formats (Avro, JSON, Protobuf)
* Validate topics, partitions, offsets, retention, and schema evolution
* Simulate late events, duplicates, and failure scenarios
Automation & Frameworks
* Build Python-based data testing frameworks
* Develop reusable validation utilities and synthetic datasets
* Integrate data tests into CI/CD pipelines
* Enable automated alerts for data quality issues
Performance & Reliability
* Validate throughput, latency, and concurrency at scale
* Test retry logic, idempotency, and recovery mechanisms
* Perform regression, soak, and failover testing
Observability
* Validate logs, metrics, and alerts using tools such as CloudWatch, Prometheus, and Grafana
* Define and monitor data SLAs and SLOs
* Support incident response, root cause analysis, and postmortems
Required Qualifications & Experience
* 7+ years of total experience in QA, SDET, or Data Quality Engineering
* Minimum 4–6 years of hands-on experience working with data platforms, data pipelines, or data engineering ecosystems
* 3+ years of hands-on experience with Databricks and Apache Spark
* Strong SQL skills for data validation, reconciliation, and complex analysis
* Proficiency in Python for automation and data validation
* Experience testing ETL/ELT pipelines (batch and streaming)
* Hands-on experience with Kafka or similar streaming platforms
* Strong understanding of AWS data services (S3, Glue, Lambda, Redshift, Athena)
* Experience working with large-scale distributed data systems
* Strong debugging, analytical, and problem-solving skills
Nice to Have
* Experience with data quality or observability tools such as Great Expectations or Monte Carlo
* Knowledge of schema registry and data contracts
* Experience with CI/CD tools such as GitHub Actions or Jenkins
Flexible work from home options available.
Compensation: $62.00 - $65.00 per hour
About Us
We work to deliver profitability in your business – with effective communication, consulting, and interactive solutions. Following an Agile Work Approach, we make sure you get the ideal solutions at minimum expenses.
Work Approach
Our Philosophy
Our Philosophy starts-and-ends at the Client-first approach. Be it understanding your business requirements to choosing the right technologies, we work as a collective team that takes all the possible steps to grow continuously towards our common goal.
Our Philosophy starts-and-ends at the Client-first approach. Be it understanding your business requirements to choosing the right technologies, we work as a collective team that takes all the possible steps to grow continuously towards our common goal.
Work Policy
We promote a collaborative work environment. We involve everyone working in the organization in community decisions and encourage them to think from a broader perspective. Our work process promotes flexibility and we maintain a high level of discipline at different levels of execution.
The Future
SelectMinds have years of experience in the domain helps us understand the need-of-the-hour better. This understanding drives us to a better future with every minute ticking. We believe we will be taking off major businesses from their flagship positions, with the products we are eyeing today.
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