Hiring a Databricks Data Engineer Associate in 2024? Look for these 10 skills

Introduction

In the rapidly evolving field of data engineering, staying updated with the latest skills is crucial. As companies increasingly recognize the value of big data, the demand to hire Databricks developers continues to grow. In case you are looking to hire Certified Databricks Data Engineer Associate you would need to look for a very diverse range of skills from understanding the Lakehouse platform to implementing production pipelines. Below is the list of the top 10 skills you should look for.

1. Platform Understanding

A top Databricks Data Engineer Associate should have a comprehensive understanding of the Databricks Platform, which integrates advanced data management and analytics capabilities. Key components include:

• Databricks Workspace: The central hub for collaborative data engineering and data science tasks, featuring tools for managing files, folders, and notebooks.
• Apache Spark Integration: Mastery of Spark, which powers data processing tasks in Databricks, is essential for handling large-scale data efficiently.
• Cloud Integration: Expertise in configuring Databricks with cloud services like AWS, Azure, and GCP for seamless data ingestion and storage.

Azure Databricks provides a robust platform for building production data pipelines using Apache Spark. It offers key features such as scalability, compatibility with various data stores, and performance optimization. 

Problem Solved: Enhances team collaboration and streamlines project workflows, leading to faster project completion and more effective teamwork.

2. Building ELT Pipelines

An effective Databricks data Engineer Associate should excel in developing efficient ELT (Extract, Load, Transform) pipelines:

• Spark SQL: Proficiency in Spark SQL for querying and managing data is crucial.
• Python Skills: Utilizing Python libraries such as Pandas and NumPy for data manipulation complements Spark SQL, facilitating robust pipeline development.

Problem Solved: Streamlines data querying and manipulation, making data more accessible and usable for analysis and reporting.

3. Relational Entities & Python skills

Managing relational data structures and acquiring essential Python skills are foundational for a Databricks Data Engineer Associate.

• Relational Data Management: Understanding how to work with relational databases, including knowledge of SQL, ensures that you can handle structured data effectively. This skill is crucial for tasks like data normalization, indexing, and performing complex joins.
• Python Proficiency: Python is a versatile programming language widely used in data engineering. For a Databricks Data Engineer Associate, it’s vital to have proficiency in Python basics.

Problem Solved: Supports a wide range of data processing tasks, increasing the efficiency and effectiveness of data workflows.

4. Manipulating Data

Mastering data transformation techniques and best practices are key for a good Databricks data engineer:

• Data Cleansing: Removing invalid data to ensure accuracy.
• Data Aggregation: Summarizing large datasets for insightful analysis.
• Data Enrichment: Enhancing data with external information.
• Best Practices: Implement version control, thorough documentation, and rigorous testing to maintain high-quality data projects.

Problem Solved: Adds value to existing data, leading to more comprehensive and insightful analysis. Provides aggregated views of data, making it easier to identify trends and patterns.

5. Structured Streaming

Handling real-time data is critical for modern data engineering:

• Structured Streaming: Knowledge of managing batch and streaming workloads, state management, and real-time analytics is essential.
• Applications: Implementing real-time dashboards and alert systems based on data patterns.

Problem Solved: Provides up-to-date insights and alerts, improving decision-making and operational responsiveness.

6. AutoLoader and Multi-hop Architecture

Should have Experience with AutoLoader and Multi-hop Architecture

• AutoLoader: Using AutoLoader for efficient incremental data ingestion is crucial for managing high-velocity data sources.
• Multi-hop Data Pipelines: Designing pipelines with stages such as Bronze (raw data), Silver (cleaned data), and Gold (aggregated data) ensures comprehensive data processing.

Problem Solved: Enhances data quality and processing efficiency, leading to better analytics and insights.

7. Delta Live Tables

Databricks data engineer should have Proficiency in Delta Live Tables

Building and managing Delta Live Tables to automate data ingestion, ensure data quality, and handle end-to-end data lifecycle effectively.

Problem Solved: Simplifies data pipeline management and ensures data consistency and reliability throughout its lifecycle.

8. Building Production Pipelines

They must be capable in Building Production Pipelines

• Production Pipelines: Developing robust, production-grade pipelines with features like job scheduling and error handling.
• Databricks SQL and Dashboards: Integrating SQL queries and creating interactive dashboards for real-time data visualization.

Problem Solved: Provides actionable insights and enhances data accessibility for decision-makers.

9. Workflows and Dashboards

Skills in Workflows and Dashboards

• Workflows: Automating and orchestrating data engineering tasks through efficient workflows.
• Dashboards: Designing and deploying interactive dashboards that provide up-to-date insights using visualization tools like Power BI or Tableau.

Problem Solved: Offers intuitive data visualizations, aiding in quick and informed decision-making.

10. Unity Catalog and Entity Permissions

Ensuring data governance and managing security through Unity Catalog and Entity Permissions are fundamental skills.

• Data Governance with Unity Catalog: Implementing data governance by tracking data lineage and classifying sensitive data.
• Entity Permissions: Managing access controls through Role-Based Access Control (RBAC) and maintaining audit logs to ensure data security and compliance.

By hiring a Databricks Data Engineer with these skills, businesses can improve their data management capabilities, enhance operational efficiency, and make more informed decisions based on accurate and timely data.

Problem Solved: Protects sensitive data and ensures that data access is properly controlled and audited.

How Do I Hire a Databricks Data Engineer?

To Hire Databricks developers, it involves a few key steps:

1. Connect with Experts: Engage with Client Success Experts to understand your project needs.
2. Define Requirements: Collaborate with Technical Advisors to outline project requirements, team structure, and costs.
3. Curate Candidates: Shortlist pre-vetted Databricks data engineers who align with your needs.
4. Interview and Select: Assess candidates through technical interviews to find the best fit.
5. Onboard Smoothly: Streamline the onboarding process with the help of Client Success Experts, ensuring a seamless integration.

Following these steps ensures you find the right talent for your project.

Conclusion

In the dynamic world of data engineering, hire Databricks developers who possess the right skills is crucial for driving your company’s success. These professionals bring expertise in mastering the Lakehouse platform, implementing robust production pipelines, and leveraging Databricks’ powerful tools. By investing in skilled Databricks developers, you ensure your data initiatives are managed effectively, keeping your organization competitive and innovative. As the demand for proficient data engineers continues to grow, securing top talent will be key to unlocking your data’s full potential.

Databricks

Bhargav Bhanderi

Director - Web & Cloud Technologies