Responsibilities:
- Design, develop, and maintain automated test frameworks, tools, and test cases for Data Engineering and Data Warehouse - applications.-
- Collaborate w- ith cross-functional teams, including software developers, data engineers, and data analysts, to ensure - comprehensive- testing coverage and adherence to quality standards.-
- Conduct thoro- ugh testing of data pipelines, ETL processes, and dat- a transformations using Big Data technologies such - as Spark, - Hive, and Imp- ala.-
- Apply your ex- pert- ise in Data Warehouse methodologies and best practices to validate the accuracy, completeness, and - performance of ou- r data storage and retrieval systems.-
- Identify, document, and track software defects, working closely with the development team to ensure timely resolution.
- Participate in code reviews, design discussions, and quality assurance meetings to provide valuable insights and contribute to the overall improvement of our software products.
Requirements:
- Bachelor's or Master's degree in Computer Science, Engineering, or a related field.
- Strong programming skills in Java. Proficiency in other languages such as Python is a plus.
- Extensive experience 6-9 yrs in dev/testing Data Engineering and Data Warehouse applications.
- In-depth knowledge and hands-on experience with Big Data technologies like Spark, Hive, and Impala.
- Thorough understanding of Data Warehouse methodologies, applications, and processes.
- Excellent problem-solving and debugging skills, with a keen eye for detail.
- Strong communication and collaboration skills to work effectively in a team-oriented environment.
- Experience with version control systems, continuous integration, and test automation frameworks is highly desirable.
- Hands-on experience with Apache NiFi is a plus.
- Experience with ETL (Extract, Transform, Load) tools is required, with a strong understanding of data integration and data transformation processes. (Informatica/Talend/Data Stage)
- Familiarity with data ingestion, data cleansing, data validation, and data enrichment techniques within ETL processes.
- Understanding of parallel processing and performance optimization techniques in ETL workflows.
- Ability to work with various data formats and sources, including structured, semi-structured, and unstructured data.
- Experience in working with large-scale data sets and distributed data processing frameworks is a plus (e.g., Hadoop, Spark).
Additional Skills for Senior Role:
- Demonstrated leadership abilities, including the ability to mentor and guide junior team members.
- Proven experience in defining and driving the overall testing strategy for Data Engineering and Data Warehouse projects.
- Strong analytical and critical thinking skills to identify process improvements and drive efficiency.
- Experience in managing and coordinating testing efforts across multiple projects and stakeholders.
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