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Senior Data Validation Specialist
2 weeks ago
Job Summary:
We are seeking a Quality Assurance Engineer to ensure the accuracy, integrity, and performance of our data solutions. The ideal candidate will have experience with data engineering, testing, and validation.
Key Responsibilities:
- Data Pipeline Testing: Test and validate data pipelines (ETL/ELT processes) to ensure accurate data movement, transformation, and integration across different platforms.
- Data Quality Assurance: Define and implement data quality checks, perform exploratory data testing, and monitor data for accuracy and consistency.
- Test Automation: Design and implement automated testing strategies for data validation using frameworks/tools like PyTest, SQL queries, or custom scripts.
- Collaboration: Work closely with Data Engineers, Data Analysts, and Product Managers to understand requirements and deliver test plans and strategies aligned with data engineering processes.
- Performance Testing: Analyze and test the performance and scalability of large-scale data solutions to ensure they meet business requirements.
- Defect Management: Identify, track, and resolve data quality issues and bugs, working with teams to ensure timely resolution.
- Compliance: Ensure that data engineering solutions comply with data governance, privacy, and security standards.
- Reporting: Generate testing reports and provide insights into data quality and system performance.
Required Skills & Qualifications:
- Proven Experience: 3+ years of experience as a QA Engineer, Data Engineer, or similar role in data-focused environments.
- Strong SQL Skills: Proficiency in writing complex SQL queries to validate and test data.
- ETL/ELT Experience: Familiarity with ETL/ELT tools and processes like DBT, Apache Airflow, Talend, Informatica, etc.
- Automation Frameworks: Experience with test automation frameworks and tools such as PyTest, Robot Framework, or similar.
- Cloud Platforms: Knowledge of cloud services (AWS, GCP, Azure) and tools like Redshift, BigQuery, Snowflake, or Databricks.
- Programming: Strong scripting and programming skills in Python, Java, or a similar language.
- Data Warehousing: Understanding of data warehousing concepts and best practices for data validation.
- Version Control: Experience using version control tools (e.g., Git) for code and testing artifacts.
- Agile Environment: Experience working in Agile/Scrum teams and knowledge of CI/CD pipelines.
- Attention to Detail: Meticulous when it comes to data validation, ensuring data accuracy and quality at every step.
Nice to Have:
- Big Data Experience: Exposure to big data tools such as Hadoop, Spark, or Kafka.
- Data Governance & Compliance: Familiarity with GDPR, CCPA, or other data privacy regulations.
- BI Tools: Experience working with BI tools like Tableau, PowerBI, or Looker.
- Certification: AWS/GCP Data Engineering or QA certifications.
Education:
Bachelor's degree in Computer Science, Information Systems, Data Science, or a related field.