Ever dealt with this scenario?

Your CEO wants real-time analytics, the data science team needs better pipelines, and your data warehouse is bursting at the seams. Oh – and you need someone who can handle all of that… yesterday. 

If you’ve found yourself nodding, you’re in good company. In 2025, finding a data engineer who can actually deliver feels like trying to hit a bullseye blindfolded. 

But there’s good news. While the perfect data engineer might seem elusive, they’re out there – promise!

And this guide will show you exactly how to find, attract, and hire them without losing your mind – or your budget – in the process. 

Why Hiring The Right Data Engineer Matters

Companies hire data engineers all the time. You know this. But what you probably don’t know is that a mismatched data engineering hire costs 30% of their annual salary in recruitment and replacement expenses. The wrong person can crater your entire data strategy. And when your data pipelines break, your data scientists can’t work, your data analysts can’t report, and your ability to ensure data integrity across systems crumbles. 

It’s game over. 

The impact ripples throughout your organization. Your data science team, stuck waiting for clean, transformed data, misses critical project deadlines.

Business analysts struggle with inconsistent reports, leading to delayed, or even flawed, decision-making. In the meantime, your cloud computing costs skyrocket because of inefficient data processing tools and poorly optimized queries. 

From maintaining complex data warehouses to building resilient pipelines, the right, qualified data engineers are the backbone of your data infrastructure. 

How To Hire A Data Engineer

Finding and hiring top data engineering talent requires a systematic approach, from understanding the role’s requirements to creating an effective onboarding process. Let’s break down each step of building a successful data engineering team. 

Understand The Data Engineer Profile

Gone are the days of hiring data engineers based solely on their Structured
Query Language (SQL) skills. Today’s data engineering requires a sophisticated blend of technical expertise and business acumen. Think of your ideal data engineer as an architect, contractor, and technical project manager rolled into one. They need to design robust systems, build reliable pipelines, and ensure that everything runs smoothly at scale. 

Here’s what that looks like in practice: 

Technical Foundation

Advanced SQL isn’t just about writing queries anymore. Now, data engineers need to understand query optimization, data modeling, efficient data storage strategies across different systems, and DevOps best practices. They should be comfortable working with both structured and unstructured data, knowing when to use specialized tools for each scenario. 

Python or Java proficiency goes beyond basic scripting. Your ideal candidate should understand software engineering principles, knowing how to write maintainable code that others can understand and debug. They should also be familiar with version control systems and CI/CD pipelines specific to data workflows. 

Cloud And Infrastructure Knowledge

Modern data platforms are increasingly cloud-native, requiring expertise in at least one major cloud provider like AWS, Azure, or GCP. This isn’t just about spinning up services – it’s about understanding cost optimization, security best practices, and how to architect solutions that scale efficiently. 

Beyond Technical Skills

The most successful data engineers bring more than technical expertise to the table. They need strong problem-solving abilities to troubleshoot complex issues across the entire data platform. And though this seems obvious, it has to be said: communication skills are crucial as they’ll need to explain technical concepts to non-technical stakeholders and collaborate effectively with data scientists and analysts. 

Create A Compelling Job Description

Let these five steps guide you as you craft a job description to attract top data engineering talent. 

Step 1: Lead With Impact And Culture

Open with your unique data challenges while weaving in your team’s collaborative culture. For example: 

• “Join our team of 15 data professionals transforming raw data into actionable insights through real-time streaming pipelines that process 10TB of data daily”
• “Help scale our data platform that powers machine learning models across five product lines” 
• “Work with a collaborative team that values knowledge sharing and innovative problem-solving”

Share specific examples of how your team works: weekly architecture reviews, monthly hackathons, or regular cross-training with data scientists. This is not the kind of job description where you should use cliches like: “fast-paced environment” or “rockstar engineer.” Be straightforward so you resonate with the professionals you’re targeting. 

Step 2: Specify Technical Requirements

Break down technical needs by actual usage and expertise level, like this: 

Core Technologies (Daily Use): 

• Languages: Python (PySpark), Java (required), SQL (advanced)
• Big Data: Apache Spark for large-scale processing
• Cloud: AWS (specific services: Redshift, EMR, Glue)
• Streaming: Kafka, Kinesis for real-time pipelines

Supporting Tools: 

• Orchestration: Airflow or similar workflow tools
• Version Control: Git, including CI/CD practices
• Infrastructure: Terraform for infrastructure as code
• Monitoring: Prometheus, Grafana for observability 

Step 3: Define Clear Success Metrics

Candidates need to clearly know what’s expected of them. The more details you provide, the better. Set specific, measurable goals. It saves time for everyone. 

First Quarter: 

• Reduce data pipeline processing time by 30%
• Implement three new data integration pipelines with external partners
• Migrate three critical ETL jobs to Spark, improving efficiency by 40%

First Year: 

• Design and implement real-time data processing reducing latency from hours to minutes
• Achieve 99.99% uptime for critical data pipelines
• Decrease cloud infrastructure costs by 25% through optimization

Step 4: Detail Growth And Development

If you’re hiring for the long-term, most candidates will want to know of opportunities for growth, along with how they’ll be working within a team environment. Is there a budget for continuing education? Is mentorship valued at your company? What about team building activities? Here are some ideas: 

Technical Development: 

• $5000 annual learning budget
• Dedicated sprint time for exploring new technologies
• Regular knowledge sharing sessions with data science team
• Clear path from Senior Engineer to Principal Engineer

Team Collaboration: 

• Weekly architecture discussions
• Monthly cross-team project reviews
• Quarterly hackathons
• Mentorship opportunities (both giving and receiving)

Step 5: Be Transparent About Practicalities 

Finally, what’s in it for them? What’s the interview process? How much can they expect to be paid? Do they get any special perks or benefits? Provide clear information about the following: 

Compensation And Benefits

• Salary range: $X-Y (based on experience)
• Equity: [details if applicable]
• Comprehensive health coverage
• 401(k) matching

Work Environment: 

• Hybrid schedule (3 days in office, 2 remote)
• Modern tech stack with room for innovation
• Direct collaboration with data scientist and analysts
• Focus on documentation and knowledge sharing

Interview Process: 

• Initial technical screen (60 minutes)
• Take-home project (real-world data pipeline design)
• On-site: system design and team fit discussions
• Final round with senior technical leadership

Pro tip: Every element of your job description should reflect your commitment to building a strong, collaborative data engineering team while being crystal clear about technical requirements and expectations. 

Find The Right Places Where Top Data Talent Spends Their Time

While traditional job boards still play a role in the hunt for data engineering talent, the most successful hiring managers are getting creative with their sourcing strategies. They know where to look, how to leverage the network they already have, and how to incentivize candidates beyond a basic salary offering – think flexible work environments and the ability to work with cutting-edge tools. 

Take A Community-First Approach

The best data engineers often hang out where problems are being solved. For example, active GitHub contributors to popular data tools often make excellent candidates because they’ve already demonstrated their ability to work with complex codebases. Look for engineers who contribute to projects related to data processing, pipeline orchestrations, or data quality tools. 

Scour Technical Communities 

Discord servers and slack channels that are dedicated to specific technologies have become goldmines for talent. Engineers who actively participate in these communities often stay ahead of industry trends and bring valuable network connections to your team. Popular channels often focus on tools like dbt, Airflow, or Spark. 

Leverage Your Network

Your existing data scientists and analysts often have strong connections in the data community. Companies see a 45% better retention rate with referred technical hires, likely because these candidates already understand your technical ecosystem and culture through their connections. If no one in your company knows of a potential great candidate, try leveraging your LinkedIn network and let your connections know what you’re looking for. 

Conduct Effective Interviews For Data Roles

The interview process for engineers needs to evaluate both technical skills and problem-solving approaches while maintaining fairness and objectivity. Consider structuring your interview process in the following way. 

Technical Deep Dive

Give candidates a real-world scenario from your organization. Instead of abstract whiteboard problems, use actual challenges like optimizing slow-performing pipelines, optimizing data transformation workflows, or implementing a DevOps strategy. The goal is to understand their problem-solving approach and communication style, not just their technical knowledge. 

System Design Discussion

Have candidates design a data architecture for a specific use case. This reveals their understanding of trade-offs between different data processing tools and their approach to ensuring data quality and scalability. Pay attention to how they explain technical decisions to different stakeholders. 

Ensuring Fair Evaluation

Combat unconscious bias through structured interviews and consistent evaluation criteria. Use detailed rubrics for technical assessment, rotate diverse interview panels, and document feedback immediately. Consider offering multiple formats for technical assessment to accommodate different working styles while maintaining high standards. 

Onboard Your New Data Engineer

So now, you’ve found your new data engineer. Congratulations! Where do you go from here? 

The first 90 days are crucial for setting up your new hire for success. A structured onboarding program should strike a balance between immediate productivity with long-term growth, like so: 

Week 1: Foundation Building

Start with a comprehensive overview of your data architecture. Walk through existing pipelines, data models, and known technical debt. Assign a technical mentor who can provide historical context and answer detailed questions. 

First Month: Guided Implementation

Begin with small, self-contained projects that touch different parts of your data platform. This helps new hires understand system interdependencies while delivering tangible value. Regular check-ins with both technical mentors and business stakeholders ensure alignment on expectations and deliverables. 

MSH Is Here To Help When You Need It

Finding the right data engineer shouldn’t feel like searching for buried treasure. While the world of technology continues to evolve, the fundamental challenge remains the same: connecting the right talent with the right opportunity at the right time. 

If you’re looking to build or scale your data engineering team, MSH specializes in connecting organizations with pre-vetted data engineering talent who can hit the ground running.

As one of the top data engineering consulting companies, our deep understanding of both technical requirements and cultural fit helps ensure successful, lasting placements. 

Ready to transform your data engineering hiring process? Let’s talk about finding the right fit for your team. Connect with MSH’s technical recruiting specialists to discuss your needs and challenges.