Recruiter's Glossary

Examples: Data Integration • Looker • Data Cleansing

Data Integration

Term from Data Analytics industry explained for recruiters

Data Integration is the process of combining information from different sources into one unified view. Think of it like putting together pieces of a puzzle – companies have data stored in various places (like spreadsheets, databases, and business applications), and data integration helps bring it all together so it makes sense. This is important because businesses need to see the complete picture of their operations, customers, and performance. It's similar to being a translator who helps different systems "talk" to each other and share information smoothly. When you see this term in job descriptions, it usually means the role involves helping organizations manage how their data flows between different systems and ensuring that all the information works together correctly.

Examples in Resumes

Led Data Integration projects connecting multiple customer databases, resulting in 40% faster reporting

Designed Data Integration solutions to combine sales and inventory systems

Implemented Data Integration processes for merging data from 5 legacy systems

Created automated Data Integration workflows to streamline financial reporting

Typical job title: "Data Integration Specialists"

Also try searching for:

Data Integration Engineer ETL Developer Data Integration Architect Integration Specialist Data Pipeline Engineer Data Integration Analyst Integration Developer

Where to Find Data Integration Specialists

Online Communities

Job Boards

Professional Networks

Example Interview Questions

Senior Level Questions

Q: Can you describe a challenging data integration project you managed and how you handled it?

Expected Answer: Look for answers that demonstrate leadership in complex projects, problem-solving abilities, and experience managing different stakeholder needs. They should explain how they planned the project, handled challenges, and ensured data quality.

Q: How would you approach integrating data from multiple legacy systems into a new platform?

Expected Answer: The candidate should discuss their approach to planning, risk assessment, data mapping, testing, and ensuring business continuity. They should mention experience with similar projects and strategies for managing potential complications.

Mid Level Questions

Q: How do you ensure data quality during integration processes?

Expected Answer: They should explain methods for data validation, cleaning, and error handling. Look for mentions of data quality checks, monitoring processes, and experience with data cleansing tools.

Q: What steps do you take to document data integration processes?

Expected Answer: Candidate should discuss creating clear documentation of data flows, mapping rules, and procedures. They should emphasize the importance of maintaining updated documentation for team collaboration.

Junior Level Questions

Q: What do you understand about ETL (Extract, Transform, Load) processes?

Expected Answer: Should be able to explain in simple terms how data is moved from source to destination, with basic understanding of data transformation concepts and common tools used.

Q: How would you verify that data was correctly integrated between two systems?

Expected Answer: Look for basic understanding of data validation methods, such as comparing record counts, checking key fields, and basic error detection approaches.