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ETL Developer

Roles & Responsibilities

  • Minimum 5 years of professional experience in ETL development, data integration, and data warehousing.
  • Proficiency in ETL tools and frameworks such as Apache NiFi, Apache Spark, Talend, Informatica, or similar technologies.
  • Strong SQL skills for data extraction, transformation, and loading tasks.
  • Experience working with relational databases (e.g., SQL Server, Oracle, PostgreSQL) and understanding of data modeling concepts.

Requirements:

  • Data Extraction: Collaborate with business analysts and data source owners to understand data requirements and extract data from diverse sources such as databases, APIs, flat files, and external systems.
  • Data Transformation: Design and implement data transformation logic to cleanse, enrich, aggregate, and restructure raw data, ensuring data quality, consistency, and adherence to business rules.
  • Data Loading: Develop and optimize ETL workflows to load transformed data into data warehouses, data marts, and other target systems, ensuring data accuracy and timeliness.
  • Performance Optimization: Identify and address performance bottlenecks in ETL processes, optimize data loading and transformation routines, and enhance overall data processing efficiency.

Job description

Job Title: ETL Developer 

Experience Level: 5+ years

Job Description: We are seeking an experienced ETL (Extract, Transform, Load) Developer to join our data engineering team. As an ETL Developer, you will play a crucial role in designing, developing, and maintaining efficient and scalable data pipelines that enable the extraction, transformation, and loading of data from various sources into our data warehouse and other data storage solutions. Your expertise in ETL processes and data integration will be instrumental in ensuring the availability of high-quality data for analysis and reporting.

Key Responsibilities:

  1. Data Extraction: Collaborate with business analysts and data source owners to understand data requirements and extract data from diverse sources such as databases, APIs, flat files, and external systems.
  2. Data Transformation: Design and implement data transformation logic to cleanse, enrich, aggregate, and restructure raw data, ensuring data quality, consistency, and adherence to business rules.
  3. Data Loading: Develop and optimize ETL workflows to load transformed data into data warehouses, data marts, and other target systems, ensuring data accuracy and timeliness.
  4. Performance Optimization: Identify and address performance bottlenecks in ETL processes, optimize data loading and transformation routines, and enhance overall data processing efficiency.
  5. Data Quality and Validation: Implement data validation and quality checks to ensure the accuracy, completeness, and reliability of data throughout the ETL process.
  6. Error Handling: Develop mechanisms for handling and logging data load errors, exceptions, and anomalies, and implement error recovery strategies to maintain data integrity.
  7. Documentation: Create and maintain technical documentation, including ETL design specifications, data flow diagrams, and process documentation, to facilitate knowledge sharing and future development efforts.
  8. Collaboration: Work closely with data engineers, database administrators, and business stakeholders to understand data requirements, resolve issues, and deliver high-quality ETL solutions.
  9. Version Control: Utilize version control systems to manage ETL code, scripts, and configurations, ensuring proper version tracking and collaborative development practices.
  10. Monitoring and Maintenance: Monitor ETL jobs, troubleshoot issues, and perform routine maintenance tasks to ensure the reliability and availability of data pipelines.

Qualifications:

  • Minimum 5 years of professional experience in ETL development, data integration, and data warehousing.
  • Proficiency in ETL tools and frameworks such as Apache NiFi, Apache Spark, Talend, Informatica, or similar technologies.
  • Strong SQL skills for data extraction, transformation, and loading tasks.
  • Experience working with relational databases (e.g., SQL Server, Oracle, PostgreSQL) and understanding of data modeling concepts.
  • Knowledge of data warehousing concepts and dimensional modeling techniques.
  • Familiarity with scripting languages (e.g., Python, Bash) for data processing and automation.
  • Understanding of data governance, data quality, and data security best practices.
  • Excellent problem-solving skills and attention to detail.
  • Effective communication skills and the ability to collaborate with cross-functional teams.
  • Experience with cloud-based data solutions (e.g., AWS, Azure, Google Cloud) is a plus.


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