
▷ [04/10/2025] Data Engineer
3 weeks ago
Responsibilities
- Automate data quality checks and validation processes using SQL, Python, and data testing frameworks.
- Perform reconciliation, integrity, and transformation testing across data platforms.
- Work with AWS SageMaker Studio (Unified Studio) for validating ML/data workflows and integrations.
- Validate data flows on cloud platforms (AWS, Azure).
- Integrate automated data validation into CI/CD pipelines.
- Collaborate with development, data engineering, and analytics teams to align on standards, governance, and quality.
Qualifications
- 4 + years of Experience in Data Engineering.
Required Skills
- Strong QA background with experience in data quality testing and automation.
- Hands-on with Amazon SageMaker Studio (Unified Studio) – must have.
- Advanced SQL for reconciliation and validation of large datasets.
- Scripting skills in Python (PyTest, PySpark, automation scripts).
- Practical experience with cloud data platforms:
- AWS (S3, Glue, Redshift, Athena, SageMaker Studio).
- Familiarity with data testing frameworks (Great Expectations, dbt tests, QuerySurge).
- Knowledge of CI/CD tools (Jenkins, GitHub Actions, Azure DevOps).
Preferred Skills
- Exposure to BI/reporting validation (Power BI, Tableau, Looker).
- Understanding of data governance and metadata management.
- Knowledge of big data ecosystems (Spark, Hadoop).
- API testing experience for data services.
Certifications (Preferred/Plus)
- AWS Certification (AWS Certified Data Analytics, AWS Machine Learning Specialty, or Solutions Architect).
- Microsoft Azure Data Engineer Associate / Google Cloud Professional Data Engineer.
- Snowflake SnowPro Certification.
- Sage Maker Certification.
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