Gone are the days of data engineers toiling away in the background. The rise of AI and data’s growing importance is mirrored by data engineers’ growing value. Great data requires great data engineering, so any business that relies on data to make decisions needs to know how to correctly scale its data engineering team.
This guide walks you through how to know when it’s time to scale a data engineering team, how to determine the specific skills your startup needs, and how to build a hiring process for data engineers that attracts top talent.
How to Know It’s the Right Time to Scale
Expanding your data team too early can lead to inefficiencies and unnecessary costs, while waiting too long to scale can slow down your company’s growth. Before you start hiring, be sure to assess whether it’s really the right time to scale.
Signs It’s Time to Scale
Data Processing Bottlenecks: Your current team is struggling to process increasing amounts of data on schedule due to frequent delays in ETL (Extract, Transform, Load) processes and other data pipeline performance issues.
Increased System Downtime or Failures: Your company experiences frequent outages or slow response times as a result of data pipeline failures such as duplicates, missing values, or inconsistent formats.
Growing Company Complexity: New products, services, or markets are increasing your startup’s data demands beyond what your current data engineering team can handle.
More Compliance and Security Needs: Changes to regulatory requirements around data privacy and security may require specialized engineers to ensure compliance.
Too Many Data Requests: Your data engineering team is constantly overwhelmed with internal data requests.
Ballooning Technical Debt: When data engineers lack the time to tackle technical debt, system stability can start to suffer.
Understanding the Skills You Need
Before you start the hiring process, you need to understand which skills are missing in your current data engineering team. Here’s are five steps to help you determine which roles to hire:
Identify Pain Points and Bottlenecks: Evaluate your current team's workload and where they are struggling. Use the common signs it’s time to scale listed above to inform where the team’s pain points are.
Audit Your Current Skill Set: Conduct an assessment of your current data engineering team members’ job roles, skill sets, and specific strengths.
Define Business Goals and Roadmap: Hiring should align with your company’s long-term data strategy. Ask yourself questions such as:
Are we planning to integrate machine learning or advanced analytics? If so, you may need ML Engineers or MLOps Engineers.
Are we expanding our data warehouse capabilities? A Data Warehouse Engineer could be valuable.
Do we need real-time data processing? A Streaming Data Engineer with Kafka or Flink experience might be the right choice.
Consult Stakeholders: Talk to your company’s data consumers (data scientists, analysts, product managers) to understand what gaps exist in your data infrastructure and how new hires could help.
Prioritize Hiring Based on Impact: Once you’ve identified the roles your data engineering team needs to scale, prioritize hiring based on:
Urgency: Which role will immediately remove bottlenecks?
Business Impact: Which hire will contribute the most to company goals?
Budget & Resources: Can the current team upskill, or is a hire necessary?
Programming Languages and Team Member Skills
Data engineering has a wide scope and uses a variety of tools, each with their own niche within the practice. Choose the right programming languages and ensure job descriptions align with industry standards to scale successfully.
Python – Popular for data processing and machine learning applications.
SQL – Essential for working with relational databases.
Scala – Often used for big data processing frameworks like Apache Spark.
Java – Useful for large-scale data applications and enterprise environments.
Go – Gaining popularity for high-performance data applications.
Cloud Platforms – AWS, Azure, and Google Cloud Platform (GCP) expertise is crucial for modern data engineering roles.
Data Pipeline Tools – Apache Airflow, Kafka, and dbt are widely used.
Big Data Technologies – Spark, Hadoop, and Snowflake expertise may be necessary depending on your data infrastructure.
Data engineering requires a mix of technical and problem-solving skills, but the ability to communicate technical information to non-technical (or semi-technical) people is also a very valuable skill for data engineers.
A Hiring Process That Attracts Top Talent
Data engineers are in high demand. This means attracting top data engineering talent requires a well-structured hiring process that appeals to the best candidates in the field.
Finding the Right Recruiters
Hiring data engineers is different from hiring general software engineers. Your recruiters need to understand the specific skills required for the role. Partnering with specialized recruiters or training your HR team in technical hiring can make a big difference in the quality of candidates you receive.
You ideally want a recruiter that is familiar with data engineering concepts and terminology, has experience hiring for similar technical roles, and has the ability to engage passive candidates who may not be actively job-seeking.
Meaningful Work Benefits to Consider Offering
A competitive salary is just the beginning. When competing for experienced data engineers, offering the right mix of benefits can help you stand out. Consider the following five types:
Equity and Bonuses: Equity or stock options for long-term incentives, and annual bonuses tied to company and individual performance.
Flexible Work Arrangements: Remote or hybrid work options can be appealing to data engineers. You could also offer flexible hours to accommodate different schedules and work preferences, as well as work-from-home stipends for equipment and high-speed internet connectivity.
Career Growth & Learning Opportunities: Education stipends for certifications (AWS, GCP, Snowflake, etc.), conference allowances, internal mobility opportunities and dedicated mentorship programs with senior engineers or leadership.
Cutting-Edge Tech: Access to the latest tools (not just legacy systems), challenging projects that push innovation, and opportunities to work with AI & cloud technologies.
Strong Work-Life Balance & Wellness Perks: Generous PTO or unlimited vacation policies, paid parental leave (for both parents), and wellness stipends (gym memberships, therapy, meditation apps).
Asking the Right Questions to the Right People
Finding the right candidates means conducting effective interviews. The key to achieving this is a focus on both technical skills and cultural fit. Here are key questions to consider asking:
Technical Interview Questions
Data Pipeline Optimization – “How would you optimize a batch processing pipeline that’s taking too long to execute?”
SQL Query Performance – “How would you improve the performance of a complex SQL query?”
Cloud Infrastructure – “Can you describe the advantages of using AWS Redshift vs. Google BigQuery?”
Data Modeling – “How would you design a schema for a recommendation engine?”
Coding Challenge – Provide a short live-coding challenge that simulates real-world tasks.
Cultural Fit Questions
“How do you approach collaboration with data scientists and analysts?”
“Tell me about a time you had to debug a major data pipeline issue. How did you handle it?”
“What excites you about data engineering?”
A carefully structured interview process will ensure that you hire the right people without wasting time on candidates who aren’t a good fit.
The right-sized data engineering team ensures data can continue to be used to optimize your company’s performance. And larger, specialized data engineering teams can handle more complex data projects, giving your startup an edge. Follow these guidelines to ensure that scaling your data engineering team is efficient, effective, and gets your startup where it needs to be.