Testing Data

I. Education and Certifications

• Bachelor's Degree: A bachelor's degree in computer science, Information Technology, or a related field is generally required. Some companies may consider candidates with degrees in mathematics, statistics, or engineering if they have relevant experience.
• Relevant Certifications (a plus):
  • ISTQB Certified Tester Foundation Level
  • ISTQB Certified Tester Advanced Level – Test Analyst
  • Certified Data Management Professional (CDMP)
  • AWS Certified Data Analytics – Specialty
  • Microsoft Certified: Azure Data Engineer Associate
  • Cloudera Certified Data Engineer

II. Technical Skills

• Database Knowledge:
  • Strong understanding of relational database management systems (RDBMS) such as Oracle, SQL Server, MySQL, PostgreSQL.
  • Experience with NoSQL databases (e.g., MongoDB, Cassandra) is a plus, especially for Big Data testing.
  • Proficiency in writing complex SQL queries for data extraction, manipulation, and validation.
  • Understanding of database concepts like normalization, indexing, and stored procedures.
• Data Warehousing and ETL:
  • Knowledge of data warehousing concepts (e.g., star schema, snowflake schema).
  • Experience testing ETL (Extract, Transform, Load) processes using tools like Informatica, DataStage, or cloud-based ETL services (e.g., AWS Glue, Azure Data Factory).
  • Ability to validate data transformations and ensure data quality during ETL processes.
• Big Data Technologies (if applicable):
  • Familiarity with Hadoop ecosystem (e.g., HDFS, MapReduce, Hive, Pig).
  • Experience testing Big Data applications and data pipelines.
  • Knowledge of Spark for data processing and analysis.
• Programming/Scripting:
  • Proficiency in at least one scripting language like Python or Shell scripting for test automation and data validation.
  • Experience with programming languages like Java or Scala can be beneficial, especially for Big Data testing.
• Cloud Computing (if applicable):
  • Experience with cloud platforms like AWS, Azure, or Google Cloud.
  • Ability to test data solutions deployed in the cloud.
  • Knowledge of cloud-based data services (e.g., AWS S3, Azure Blob Storage, Google Cloud Storage).
• Testing Tools:
  • Experience with test management tools (e.g., TestRail, Zephyr).
  • Proficiency in using SQL Developer, Dbeaver or similar tools for database querying and validation.
  • Familiarity with data profiling tools (e.g., Informatica Data Quality, Trillium).
  • Experience with test automation frameworks and tools is a plus.

III. Data Testing Specific Skills

• Data Quality Assurance:
  • Deep understanding of data quality principles and methodologies.
  • Ability to define data quality rules and metrics.
  • Experience in identifying and resolving data quality issues.
• Data Validation:
  • Ability to validate data accuracy, completeness, consistency, and integrity.
  • Experience in performing data profiling and data analysis.
  • Proficiency in writing SQL queries and scripts for data validation.
• Test Case Design:
  • Ability to design comprehensive test cases based on data requirements and specifications.
  • Experience in creating test data for various scenarios.
  • Understanding of different testing techniques (e.g., boundary value analysis, equivalence partitioning).
• ETL Testing:
  • Experience in testing ETL processes and data transformations.
  • Ability to validate data mappings and transformations.
  • Proficiency in using ETL tools and technologies.
• Reporting and Documentation:
  • Ability to document test plans, test cases, and test results clearly and concisely.
  • Experience in generating test reports and metrics.

IV. Soft Skills

• Analytical Skills: Strong analytical and problem-solving skills to identify and resolve data-related issues.
• Communication Skills: Excellent written and verbal communication skills to effectively communicate with developers, data analysts, and other stakeholders.
• Attention to Detail: Meticulous attention to detail to ensure data accuracy and quality.
• Teamwork: Ability to work effectively in a team environment.
• Adaptability: Ability to adapt to changing priorities and learn new technologies quickly.
• Domain Knowledge: Understanding of the specific industry or domain for which the data is being tested (e.g., finance, healthcare, e-commerce) is a significant advantage.