Data Engineer :Dremio, SQL, MongoDB

As a Data Engineer at Talent Worx, you'll be responsible for designing and implementing data models, pipelines, and workflows utilizing Dremio, SQL, and MongoDB. You'll collaborate with data scientists to address data requirements, optimize queries, ensure data quality, monitor pipelines, and automate ETL processes—all vital tasks contributing to our data architecture.

To qualify for the Data Engineer role at Talent Worx, candidates should possess a Bachelor’s degree in Computer Science or a related field, along with 5 to 8 years of experience in data engineering focused on Dremio, SQL, and MongoDB. Strong collaboration skills and effective communication must also be demonstrated.

A Data Engineer at Talent Worx ensures data integrity by establishing best practices for data management, actively monitoring data pipelines, and troubleshooting any issues promptly. Through optimization of queries and promoting high standards for data quality, you will safeguard the reliability of our data.

In your Data Engineer role at Talent Worx, you will utilize Dremio, SQL, and MongoDB extensively. Familiarity with data warehousing concepts, ETL processes, and programming languages such as Python or Java will enhance your effectiveness. Experience with cloud platforms and database services is also advantageous.

Automation is crucial for a Data Engineer at Talent Worx as it streamlines ETL processes, reducing manual workload and enhancing efficiency in data extraction, transformation, and loading. By automating these tasks, you will improve overall productivity and focus on more strategic data architecture initiatives.

When answering this question, highlight specific projects where you've implemented Dremio. Discuss your understanding of its capabilities in data virtualization and how you've effectively utilized it to improve data access for analytics.

Discuss your knowledge of indexing, query rewriting, and execution plans. Provide examples of how your optimizations have led to improved performance and lower latency in data retrieval.

Focus on your approach to establishing data validation rules, maintaining logs for data changes, and using automated testing where applicable. Mention any tools or methodologies you’ve deployed for consistent quality assurance.

Describe the ETL tools and processes you’ve used. Provide examples of the data sources, types of transformations you've implemented, and how you've ensured the successful loading of clean data into target systems.

Emphasize your commitment to continuous learning through attending workshops, participating in online courses, and following relevant publications. Mention any specific trends you've recently integrated into your work.

Share a systematic approach—monitoring logs, identifying bottlenecks, checking data quality at each stage, and collaboration with team members. Provide a real instance where you successfully resolved a data issue.

Illustrate your teamwork experience, especially with data scientists and analysts. Describe successful collaborations where you translated their requirements into technical solutions that met business needs.

Describe the context of the problem, your thought process for approaching it, and the technical solutions you applied. Highlight the positive outcome and any lessons learned from the experience.

Discuss your documentation practices, including what tools you use and how you ensure comprehensive, understandable documentation. Emphasize the importance of knowledge transfer within your team.

Mention any programming languages like Python or Java that you’re comfortable with, providing examples of how you've used them in data processing, automation, or creating applications that aid your data engineering objectives.