Data Tester / Data Quality Engineer - Plano, TX (Remote)

[Sid K]

Hi Folks,

 

My client is looking for Data Tester / Data Quality Engineer for 11 Month Contract role based in Plano, TX (Remote)

 

Position: Data Tester / Data Quality Engineer

Location:  Plano, TX (Remote)

Duration: 11 Month Contract
Rate: $46/hr. on C2C

 

Role Description:

We are seeking a Data Tester / Data Quality Engineer to support a data modernization initiative, validating data pipelines and reconciling legacy (Oracle) and modern (Databricks/Lakehouse) environments. This role will be responsible for designing and executing data validation tests, ensuring data completeness, accuracy, consistency, and timeliness as datasets migrate and pipelines are rebuilt or replatformed.

You’ll work in an Agile delivery model with cross-functional teams to validate ETL/ELT transformations, ensure correct business rule implementation, and build repeatable, automated validation frameworks that scale across domains.

 

Key Responsibilities:

Data Testing & Validation

Design and execute data test strategies, test plans, and test cases for ingestion, transformation, and curated layers.

Validate data at rest and in motion across Oracle source systems and Databricks target platforms.

 

Perform source-to-target reconciliation, including:

record counts, checksum/hashing, aggregates, sampling

null/constraint checks, referential integrity, duplicates

transformation logic validation (business rules, SCD logic, dedup, enrichment)

Validate incremental loads, CDC patterns, and rerun/recovery scenarios.

 

Automation & Frameworks

Build and maintain automated data quality checks using SQL and/or Python (e.g., PySpark).

Develop reusable data validation utilities and parameterized scripts to reduce manual effort.

Integrate data tests into CI/CD pipelines where applicable (e.g., Azure DevOps, GitHub, Jenkins).

 

Defect Management & Collaboration

Log, triage, and manage defects with clear reproduction steps and root-cause hints.

Collaborate with data engineers to troubleshoot pipeline failures and data anomalies.

Partner with business stakeholders/analysts to confirm expected outcomes and acceptance criteria.

 

Documentation & Governance Support

Document test evidence, reconciliation results, and sign-off artifacts for releases.

Support data governance objectives (quality KPIs, issue tracking, lineage/metadata readiness).

 

Required Skills & Qualifications

 

Experience:

5 or more years of hands-on experience in data testing / QA / data quality engineering in data warehouse, lake, or analytics modernization initiatives.

Demonstrated experience validating data pipelines involving Oracle (source or warehouse) and Databricks (target lakehouse).

 

Technical Skills (Must Have)

Oracle SQL: complex queries, joins, aggregations, performance-aware validation queries.

Databricks: Experienced with Spark/Databricks SQL. Familiarity with Delta Lake concepts (tables, merges/upserts, partitions, time travel helpful)

 

Data validation techniques:

reconciliations, profiling, anomaly detection basics

test data creation and boundary testing for transformations

 

Python (preferred) and/or PySpark for automation and scalable validations.

Strong understanding of ETL/ELT concepts, data warehousing fundamentals, and dimensional modeling basics.

 

Tools & Ways of Working

Familiarity with Agile/Scrum, user stories, acceptance criteria, and sprint-based testing.

Experience with defect tracking tools (e.g., Jira, Azure DevOps).

Version control basics (e.g., Git).

Thanks
Sid