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9 UBA Solutions Jobs

Senior Software Development Engineer in Test Data Quality

UBA Solutions

5-10 years

Lalitpur

1 vacancy

UBA Solutions

posted 2hr ago

Job Role Insights

Key skills for the job

Manual Testing Python SQL Automation Testing Linux Administration Performance Testing

+ 4 more

Job Description

Senior Software Development Engineer in Test Data Quality

As a Sr. Software Development Engineer in Test Data Quality you will be part of the Data Engineering team responsible for automating and testing complex and critical data pipelines of various projects and services that handle data for core business processes at Monotype with the best engineering practices. In this role, you will create and own QA processes end to end with focus on automation and performance.

What you ll be doing:

5+ years of experience in automation, acceptance & integration tests.
Experience in building custom automation frameworks in Python, Java, JavaScript for automated testing and data validation
Hands on experience on any test case management tool.
Hands on experience in integration and database testing.
Experience with cloud platforms (AWS, Azure, GCP) and cloud data services (S3, Athena, Redshift, Big Query, Snowflake).
Hands-on experience with ETL tools, data pipelines and data formats (e.g. AWS data warehouse solutions incl. Glue and Apache Parquet)
Experience with data quality tools (e.g., Talend Data Quality, Informatica Data Quality) is an advantage.
Good to have experience with Power BI or similar business intelligence software
Good to have experience on JMeter or any relevant performance testing tools.
Exposure to Linux commands and Docker or Kubernetes
Deep hands-on technical expertise in all QA practices and processes
Exposure to research automation systems/applications will be a plus.
Knowledge of data governance and compliance standards (e.g., GDPR, CCPA, HIPAA) to ensure data integrity and security will be a plus.
Strong problem-solving skills, demonstrating creativity and efficiency in addressing data challenges.
Strong analytical skills and a keen eye for detail to ensure data quality and accuracy.
Ability to document and communicate data issues, test results, and recommendations clearly to both technical and non-technical stakeholders.
Experience with GIT, bitbucket, Github and the Gitflow branching model.
Experience adhering to an Agile development environment and iterative sprint cycle.
Experience in Continuous Integration/Continuous Development environments, preferably Jenkins or Github Actions
Efficient in working on different OS Windows, Linux & Mac.
Monotype is expanding globally. Proficiency in one or more of the following languages is desirable (not mandatory) for this role: German, Japanese, French, Spanish.

What you will be doing:

Data Validation: Perform validation across all stages of data pipelines (ETL) to ensure data accuracy, completeness, and consistency.
Automated Testing: Develop and maintain automated test scripts for data pipelines, migrations, and reports using testing frameworks and scripting languages.
ETL Process Validation: Validate ETL processes, ensuring accurate data extraction, transformation, and loading across systems, maintaining data integrity.
Data Quality Monitoring: Identify and track data quality issues, generate data quality reports, and collaborate with stakeholders to resolve inconsistencies.
Functional and Performance Testing: Conduct functional testing of data systems and non-functional testing to ensure scalability and system efficiency.
SQL and Mongo Expertise: Write complex queries for data validation and testing in SQL and MongoDB.
Collaboration: Work closely with data engineers, developers, and data scientists to understand data models, pipelines, and transformations, designing effective test cases.
Documentation: Prepare comprehensive documentation, including test strategies, plans, results, and findings, for both technical and non-technical stakeholders.
Process Improvement: Recommend improvements to data testing methodologies, automation workflows, and QA practices to enhance overall data quality.
Continuous Learning: Stay updated with the latest industry trends, technologies, and best practices in data testing and quality assurance.
Team Collaboration: Work effectively in a multidisciplinary environment with developers, designers, product managers, and QA testers.