Data Engineer (MySQL)

As a Data Engineer (MySQL) at Solvd Inc., your key responsibilities will include designing scalable data lake architectures, developing data ingestion pipelines, and creating efficient stored procedures and queries. You'll also be tasked with API development for data access, along with consulting clients on best practices for database performance and integrity. Your expertise in handling various data formats and ensuring data quality will be vital in this role.

To qualify for the Data Engineer (MySQL) position at Solvd Inc., you should have at least 4 years of experience in software engineering with strong proficiency in RDBMS and SQL, including indexing and query optimization. Familiarity with data ingestion tools, API development, and a solid understanding of data mapping is also required. Experience with Python, Java, or Node.js will further strengthen your application.

A Data Engineer (MySQL) at Solvd Inc. should be proficient in programming languages such as Python, Java, or Node.js. These languages are crucial for backend development, enabling you to effectively build APIs and data ingestion scripts that are efficient and maintainable in a scalable environment.

Yes, Solvd Inc. fosters a remote work environment for the Data Engineer (MySQL) position. We seek professionals who can work independently, ensuring results and contributing to our projects remotely while actively collaborating with our global team and clients.

For the Data Engineer (MySQL) role at Solvd Inc., familiarity with ETL/ELT tools, database performance tuning, and cloud-based solutions like AWS RDS or Google Cloud SQL would be beneficial. Understanding containerization tools such as Docker and real-time data processing technologies like Kafka can also set you apart and enhance your skill set.

In your response, highlight any relevant projects you've worked on involving data lake design. Discuss the key architectural considerations you took into account, such as scalability and data retrieval efficiency, and how you optimized schemas and indexing strategies.

Describing your process for data mapping and transformation should include methodologies you've used for ensuring data integrity and consistency. Discuss your experience with both structured and unstructured data, and specify any tools or programming languages that aided in this process.

When addressing database performance optimization, mention techniques such as query optimization, indexing, and partitioning. Explain how you've successfully applied these strategies in previous roles to enhance database efficiency, especially in large datasets.

Illustrate your answer by detailing a specific pipeline you've built. Discuss the data sources, tools used (like Apache NiFi or custom scripts), and how you managed data consistency and scheduling for on-demand or periodic updates.

Share your understanding of RESTful API design principles including resource identification, stateless communication, and how you ensure scalability and security. Mention any frameworks or tools you've utilized to develop these APIs and how you document them.

Detail your approach to maintaining data quality, including validation checks during ingestion and post-ingestion verification methods. Share examples from your past experiences to illustrate how you've addressed data accuracy and integrity.

In your answer, mention specific libraries or frameworks you've used for XML parsing and transformation, as well as techniques for managing formats such as CSV and Excel. Explain how these tools integrate into your data ingestion processes.

Discuss your communication style and methods for gathering client requirements. Share examples of how you actively listen and consult with clients to design solutions that meet their specific data needs and expectations.

Be prepared to talk about specific challenges involving data volume, integration complexities, or performance issues. Describe the steps you took to analyze the challenges and the strategies you implemented to resolve them successfully.

Share your insights about the benefits and challenges of using cloud database solutions. Discuss any experiences you've had with platforms like AWS RDS or Google Cloud SQL, emphasizing scalability, cost-effectiveness, and performance considerations.