C
ETL + DB Testing, Python
C
ETL + DB Testing, Python
Customer of founditEarly Applicant
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- Posted 9 hours ago
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Job Description
Responsibilities
- Design, develop, and maintain ETL processes to support data integration and transformation.
- Perform database testing to ensure data accuracy, consistency, and integrity.
- Write and execute SQL queries to validate data in various databases.
- Collaborate with data engineers and data analysts to identify data requirements and ensure data quality.
- Monitor ETL jobs and troubleshoot any issues that arise during the data processing.
- Document ETL processes and test cases for future reference and compliance.
Skills and Qualifications
- 5-6 years of experience in ETL development and database testing.
- Proficiency in Python for scripting and automation tasks.
- Strong knowledge of SQL for querying and manipulating databases.
- Experience with ETL tools such as Talend, Informatica, or Apache NiFi.
- Familiarity with database management systems such as MySQL, PostgreSQL, or Oracle.
- Understanding of data warehousing concepts and best practices.
- Ability to analyze and interpret complex data sets.
- Strong problem-solving skills and attention to detail.
More Info
Key Skills
About Company
Skills:
Database Testing., Etl, Etl Testing, DB Testing
Early Applicant
Skills:
Aws Services, Application Security, Python, Generative AI, LLMs, CI CD integration, RAG pipeline engineering, Code generation and code fixing using LLMs, Vector databases, MCP server development, Agentic workflows
Skills:
Python, Typescript, Javascript, Machine Learning, Rest Apis, Cloud Services, Docker, Sql, Ai Development, Data Analysis
Skills:
Query Optimization, Rest API Development, Css3, Angular, Database Design, Performance Tuning, Pyramid, Django, Devops, Typescript, Javascript, Html5, Flask, Python, AWS, Relational Databases, CI/CD
Skills:
text classification , model development , Large Language Models (LLMs), Sql, Nlp, Git, Python Programming, Deep Learning frameworks, embeddings, vector search, Data Analysis, Indexing, semantic retrieval, recommendation systems, knowledge retrieval, document understanding, production-grade ML applications, feature engineering, semantic search, embeddings chunking, CI/CD practices