5 skills your Databricks Data Engineer should have
As a Databricks partner, you want to deliver the very best service to your customers so they can make the most of this powerful platform—and to do that, you need the best talent on your bench.
Hiring the right Data Engineer is essential if you want the kind of successful client implementations, strong ROI, and widespread adoption of Databricks that will make your business a go-to for budding Databricks customers.
But Data Engineers require a distinct and wide-ranging skill set that includes technical know-how, soft skills, and consulting abilities to perform at their best, and sizing up those skills in a candidate can be a challenge. So, what are those crucial must-have skills that will enable your new Data Engineer to get right to work?
Here are five key capabilities every Databricks Data Engineer should have and why they’re so important to the long-term success of your team.
5 essential skills every Databricks Data Engineer should have
1. Proficiency in Apache Spark and Delta Lake
As the distributed computing engine underpinning Databricks and the force behind large-scale ETL, analytics, and ML, extensive experience with Apache Spark is a no-brainer. Spark is the backbone of Databricks, and poor Apache Spark knowledge can lead to slow queries and high costs.
To make the most of Apache Spark, a candidate needs a strong foundation in core concepts like RDDs, DataFrames, and Spark SQL. They should also be proficient in at least one programming language; Scala or Python tend to be the best bet for these kinds of roles. If they only know basic Spark SQL and have little experience tuning large-scale jobs, that’s a red flag.
Candidates should have a grasp of big data principles and data analysis techniques, too, as well as an understanding of Spark’s machine learning library (MLlib) and its streaming module.
Another technology that candidates should be familiar with is Delta Lake, the open-source, Parquet-based storage layer that powers ACID transactions, schema enforcement, and time travel to data lakes. Delta Lake is vital for production workloads and a major factor in creating reliable, high-performance data lakes.
What a strong skill set looks like
- Use Apache Spark broadcast joins, partitioning, and caching to speed up jobs
- Tune Spark configurations (`spark.executor.memory`, `spark.sql.shuffle.partitions`)
- Merge Delta Lake operations (upserts), Z-ordering (data skipping), VACUUM (file cleanup)
- Perform time travel (querying historical data) and schema evolution
- Diagnose skewed data, spills, and OOM errors
2. Good SQL and Python language skills
The primary language for querying and transforming data in Databricks, SQL skills are critical for any valuable Data Engineer. Ubiquitous in analytics and reporting, SQL skills will allow data professionals to write queries that select, filter, and aggregate data, combine data from a variety of sources, and clean and manipulate data to ensure top performance.
Another key language that a candidate should know is Python. Used for complex ETL, training machine learning models, automating workflows, and managing infrastructure, Python can be used for processing data of all kinds in Databricks.
If a candidate also knows Scala, that’s a big bonus. Scala can be used in Databricks to perform large-scale data processing and boost performance by carrying out low-level Spark tuning.
What a strong skill set looks like
- Use SQL to perform window functions (e.g., `ROW_NUMBER()`, `LAG()`)—simple `SELECT FROM table` queries without complex joins or optimizations won’t cut it
- Query optimization (predicate pushdown, partition pruning)
- Use Python to write efficient DataFrame operations (avoiding `collect()` and UDFs when possible
- Use Pandas user-defined functions (UDFs) for high-performance transformations
3. Databricks platform expertise
Of course, your candidate should have a deep working knowledge of the Databricks platform and hands-on experience using the product to address practical business challenges. Be wary; some candidates may have used Databricks as a turnkey cloud service but have little experience with features like DLT or Unity Catalog.
They should know their way around the solution and be able to leverage native features that speed up development. You’ll want to see how familiar they are with Databricks tools like Workflows, Delta Live Tables, and MLflow. If you’re working with enterprise-level clients, knowledge of data governance best practices and the use of Unity Catalog is great to have.
What a strong skill set looks like
- Build production pipelines that include task dependencies, retries, and alerting
- Create Delta Live Tables to handle declarative ETL with auto-scaling
- Set up row/column-level security and manage data lineage and sharing in Unity Catalog
- Use Cluster Optimization to configure autoscaling, spot instances, and instance types for cost savings
4. Data pipeline and ETL design
Data pipeline and ETL design skills are incredibly important when working with Databricks—these capabilities form the backbone of reliable, scalable, and maintainable data workflows. They equip candidates with the ability to build pipelines that ingest, transform, and deliver data across your data landscape, all while ensuring high performance, cost efficiency, and data quality.
Without strong ETL design skills on your team, your data pipelines can become brittle, inefficient, or difficult to debug, meaning increased cloud costs for you, delayed insights for your customers, and frustrated stakeholders across the board.
Your Data Engineer candidates should have a demonstrable ability to create scalable, sustainable data pipelines using best practices like Medallion Architecture, thorough testing, and CI/CD principles.
What a strong skill set looks like
- Explain the importance of Medallion Architecture and share examples of how they’ve implemented Bronze (raw), Silver (cleaned), and Gold (business-ready) layers within a lakehouse
- Build streaming pipelines that use Spark Structured Streaming
- Use watermarking and stateful operations in stream processing
- Utilize CI/CD and testing processes like Databricks
5. Knowledge of cloud services and DevOps
Databricks is a cloud-based platform, so naturally, knowledge of cloud services is a need-to-know for Databricks Data Engineers. Engineers must be able to configure secure, scalable environments by managing storage, identity access, and networking on your organization’s chosen cloud platform.
DevOps skills are also vital to make sure your deployments run as reliably and consistently as possible. They’ll also help achieve scalability; a top priority for partners managing multiple customers. DevOps practices such as infrastructure-as-code and CI/CD pipelines will help your Engineers automate deployments, reducing security risks, performance bottlenecks, and inflated costs in the process.
The marriage of proper cloud management and DevOps best practices will pay dividends for your organization through more efficient resource utilization, better collaboration, and the development of production-grade Databricks solutions.
What a strong skill set looks like
- Operate multiple cloud environments and their key services, like AWS (S3, IAM), Azure (ADLS, RBAC), and GCP (GCS, IAM)
- Set up AWS PrivateLink and VPC peering for secure connections
- Use Infrastructure-as-Code and other automation tools like Terraform for Databricks workspace provisioning and Databricks CLI/REST API for pipeline automation
Final hiring checklist
- Spark and Delta Lake: Can optimize jobs and leverage Delta features
- SQL, Python, and Scala: Can write efficient queries and PySpark code
- Databricks: Can use Workflows, DLT, and Unity Catalog
- Pipeline design: Follows Medallion Architecture and CI/CD best practices
- Cloud and DevOps: Can configure secure, automated deployments
Skip the hiring hassle with certified, delivery-ready Data Engineers from Revolent
As a Databricks Consulting Partner, Revolent can help you find the right people for your team with zero capital investment, helping you access certified talent that’s ready to make a difference from day one.
We are the only provider of this comprehensive Databricks training, providing Databricks partners with cost-effective, deployment-ready Data Engineers when they need them. With Revolent’s talent creation program, you can build robust teams of Databricks professionals with all the training, onboarding, and development costs covered by us.
