Position: Senior Data Automation Engineer
Company: Serious Software
Category: Data Engineer
Experience required: 5y
English level: upper

To be successful in this role, you will need a strong background in Data Engineering, specifically, Python, SQL, Google Cloud Platform (BigQuery, GoogleCloud Storage, Airflow). In addition to needing an ability to problem-solve effectively, you will need to have strong written and oral communication skills.

Technical stack:
- Python;
- Google Cloud Platform (BigQuery, GoogleCloud Storage);
- SQL;
- Infrastructure as Code (e.g. Terraform).

Requirements:
- 5-6+ years of relevant experience in data and software engineering;
- Strong programming skills with fluency in SQL and Python;
- Familiarity with automation frameworks and scripting tools;
- Knowledge of database concepts and experience working with relational databases;
- Understanding of data quality principles and experience with data validation techniques;
- Excellent problem-solving and analytical skills, with a keen eye for detail;
- Strong communication skills to collaborate effectively with cross-functional teams;
- Good communication English skills.

Responsibilities:
- Design and develop automation scripts: Design and develop
robust, scalable, and efficient automation solutions using programming languages, scripting tools, and database technologies;
- Data validation testing: Develop comprehensive test plans and test cases to validate data integrity and accuracy. Execute automated tests and analyze results to identify discrepancies, anomalies, and inconsistencies;
- Identify data issues: Utilize automation tools and techniques to identify incomplete, inconsistent, duplicate, invalid, outdated, or non-conforming data. Investigate and troubleshoot data issues to determine their root causes;
- Script maintenance and enhancements: Monitor and maintain automation scripts to ensure their effectiveness and reliability;
- Collaboration and documentation: Document automation procedures, best practices, and findings to facilitate knowledge sharing and ensure consistent data validation practices;
- Data quality assurance: Assist in developing and implementing data quality assurance processes and methodologies. Contribute to the development of data governance strategies to maintain high data quality standards.