27 Honest Big Data Engineer Salaries

As the role of data continues to grow, there's one pressing problem—there's a lot of data out there. So much data that businesses struggle to make use of it all. That's where big data engineers come in. These professionals design, build, and maintain the complex data systems that power analytics, machine learning models, and real-time decision-making across industries. 

As demand for data insights increases, so too does the need for big data engineers. Companies in finance, health care, tech, and retail are willing to pay top dollar for experts who can wrangle structured, semi-structured, and unstructured data into usable formats. But salaries can vary widely depending on location, experience, certifications, and the technologies you’ve mastered. 

So what does the average big data engineer make? And how can you bump yourself up the pay ladder? We'll break down what big data engineers earn around the country, the skills and tools they use, and how factors like certifications, cloud expertise, and real-time data experience can impact pay. 

What is a Big Data Engineer?

A big data engineer is responsible for designing, building, and maintaining the systems that store and process massive amounts of data. They’re the bridge between raw data and the analysts, data scientists, and business leaders who rely on it for insights. You might see similar job requirements under job listings with titles like: 

• Data Platform Engineer

• Data Infrastructure Engineer

• Hadoop/Spark Engineer

• ETL Engineer

Core responsibilities for this role can vary by industry and company. In most roles, you'll be responsible for tasks like:

• Data Pipeline Development: Creating reliable systems for collecting, transforming, and loading (ETL/ELT) data from multiple sources.

• Architecture Design: Building scalable, fault-tolerant platforms that can handle high-volume data.

• Performance Optimization: Ensuring data processing jobs run efficiently and meet business deadlines.

• Cross-Team Collaboration: Working closely with data analysts, scientists, and application developers to make sure data is accessible and usable. 

We'll dig into must-have tools and programming languages in detail later, but most big data engineers can expect to work with: 

• Frameworks: Hadoop, Spark, Flink for distributed data processing

• Pipelines & Workflow Orchestration: Apache Airflow, NiFi, Kafka

• Cloud Platforms: AWS Glue, GCP Dataflow, Azure Synapse

• Storage Systems: Amazon S3, HDFS, Snowflake, Redshift

• Programming Languages: Python, Java, Scala, SQL for building and managing data workflows

• Best Practices for: Data modeling, security, governance, and compliance

27 Honest Salaries for Big Data Engineers

In the tech world, many different factors can impact pay—and that is doubly true for big data engineers. To better understand what big data engineers actually earn across the U.S., we pulled compensation data from Glassdoor, Indeed, and Payscale and averaged the results to create a reliable salary range for each location. 

The numbers reflect base salaries only, excluding bonuses, stock options, or other perks that can significantly increase total compensation, especially in tech-heavy markets. Note that we broke the salaries for each city into low, average, and high-end ranges to account for the broad ranges of compensation. Here's what you can expect to earn as a big data engineer across the United States:  

Big Data Engineer Salary Trends and Analysis

So, what does the data tell us? It will come as no surprise that salaries for big data engineers are highest in major tech hubs like San Francisco, Seattle, and New York, where both the demand for data talent and the cost of living are sky-high. These cities see average salaries in the $160,000–$170,000 range, with top earners pushing past $200,000. 

Mid-sized cities with growing tech scenes—think Austin, Denver, and Boston—also offer strong compensation, with a mid-range of $140 to $150,000. These towns also offer a slightly lower cost of living, making them desirable locations for many who don't want to deal with the high cost of living in bigger metros. 

Lower-cost regions like Tallahassee, Cleveland, and Pittsburgh still provide competitive pay, especially for remote workers or those early in their careers, though the high end of the range rarely exceeds $160,000. 

One notable trend is the narrowing gap between mid-tier and top-tier markets for experienced engineers. Cloud specialization, expertise in real-time streaming platforms, and work in data-heavy industries like finance and healthtech can elevate salaries significantly.  

Salary Considerations for Big Data Engineers

While location matters when looking at salaries, it’s far from the only factor. The technologies you’ve mastered, the industry you work in, and the complexity of the data environment all have a direct impact on your earning potential. Employers are willing to pay a premium for skills that are rare, difficult to master, or critical to their operations. Here are the top salary considerations outside of location: 

Cloud Platform Specialization

Deep expertise in AWS, Azure, or GCP data services, such as AWS Glue, Azure Synapse, or BigQuery, can push salaries higher, especially for engineers who design and manage large-scale data environments.

Real-Time Data Systems 

Proficiency with streaming tools like Apache Kafka, Apache Flink, or AWS Kinesis adds significant value for roles focused on real-time analytics, fraud detection, or live user personalization.

Data Security and Governance

Engineers who can implement data privacy measures, lineage tracking, and compliance frameworks (such as HIPAA or GDPR) are in high demand, particularly in industries with sensitive information.

Industry 

Certain industries with specific data needs, like finance, healthtech, adtech, and e-commerce, tend to offer higher pay for big data engineers. This is due to the skill required to manage the scale, complexity, and regulatory requirements of their data environments.

Want to grow your big data engineer career? Check out our AWS Certified Data Engineer - Associate (DEA-C01) Online Training.

How Experience Impacts Big Data Engineer Salary

Like most tech roles, salaries for big data engineers rise sharply with experience and not just years on the job, but in the depth and breadth of the projects you’ve handled. Moving from entry-level to senior positions often means taking on more architecture design, leadership responsibilities, and oversight of complex, mission-critical systems. 

While your salary level might vary by location, industry, and company, here's a broad look at how your salary will likely scale over time: 

• Entry-Level (0–2 Years) ~$90,000–$110,000: At this level, you'll likely assist with data ingestion, build basic batch pipelines, and support existing workflows under senior guidance.

• Mid-Level (3–5 Years) ~$115,000–$140,000: As your skills grow, you'll be responsible for developing and maintaining production-ready pipelines, ensuring scalability, and start to take ownership of specific systems or projects.

• Senior-Level (6+ Years) ~$140,000–$180,000+: As your experience grows, you'll be tasked with leading architecture design, managing streaming platforms, mentoring junior engineers, and overseeing data infrastructure strategy.

Must-Know Tools for Big Data Engineers

The big data industry evolves quickly, but the core platforms and frameworks remain the same for most engineering roles. Developing expertise in these tools not only helps you excel in your day-to-day work but can also help you get promoted and qualify for higher-paying opportunities.

Frameworks

Hadoop, Spark, and Flink are the workhorses of distributed data processing. Hadoop remains relevant for batch processing and large-scale storage, while Spark offers faster, in-memory computation for analytics and machine learning pipelines. Flink specializes in real-time and event-driven data processing, making it essential for streaming-heavy environments.

Pipelines

Apache Airflow, NiFi, and Kafka help manage how data moves and transforms across systems. Airflow is widely used for orchestrating ETL workflows, NiFi excels at data routing and transformation, and Kafka dominates the streaming data space, enabling real-time analytics and message processing at scale.

Cloud

AWS Glue, GCP Dataflow, and Azure Synapse bring big data capabilities to the cloud, allowing teams to scale without heavy on-prem infrastructure. Glue simplifies serverless ETL, Dataflow handles both batch and streaming processing in GCP, and Synapse integrates analytics with Microsoft’s ecosystem for seamless querying and reporting.

Storage

Amazon S3, HDFS, Snowflake, and Redshift are critical for housing and querying massive datasets. S3 offers cost-effective object storage, HDFS supports high-throughput distributed storage, Snowflake provides cloud-native data warehousing with elastic scaling, and Redshift is optimized for fast SQL analytics at scale.

Monitoring

Datadog, Prometheus, and custom dashboards ensure the health and reliability of big data systems. Datadog offers deep integrations for performance monitoring, Prometheus provides open-source metrics collection and alerting, and custom dashboards can be tailored for pipeline-specific KPIs and alerts.

Must-Have Certifications for Big Data Engineers

While experience is critical in the big data space, certifications can validate your skills and give you an edge in competitive job markets. The right cert can also help you pivot into specialized roles or justify a higher salary.

Google Professional Data Engineer

This cert is ideal for engineers working in or transitioning to GCP environments. This certification tests your ability to design, build, and operate data processing systems, ensure data quality, and enable data-driven decision-making. It’s highly respected for demonstrating cloud-based big data expertise.

AWS Certified Data Engineer - Associate

Launched in 2024 as the successor to the retired Data Analytics – Specialty certification, this AWS credential validates your ability to design, implement, secure, and maintain data pipelines using core AWS services. It’s a current, highly relevant cert for any data engineer working in AWS-heavy environments.

Databricks Certified Data Engineer Associate/Professional

This cert is ideal for those working with Spark and Databricks’ cloud-based analytics platform. The associate level covers fundamental data engineering concepts, while the professional level dives deeper into advanced transformations, optimization, and platform integrations.

Cloudera Certified Data Engineer

This program is designed for engineers maintaining legacy Hadoop environments or working with Cloudera’s hybrid cloud platform. It demonstrates strong skills in data ingestion, transformation, and pipeline orchestration in large-scale, on-premise or hybrid setups.

How to Increase Your Salary as a Big Data Engineer

Big data engineering is evolving at lightning speed, and staying ahead of the curve is the best way to increase your salary. By expanding your skills and taking on higher-impact responsibilities, you can position yourself for faster salary growth. Here are a few key ways to boost your pay: 

• Specialize in Real-Time Data Pipelines or Hybrid Cloud Environments: Expertise in technologies like Kafka, Flink, and Kinesis, especially when paired with multi-cloud deployment skills, can help you command premium pay in industries that rely on low-latency analytics.

• Earn Certifications in Top Cloud Platforms and Big Data Frameworks: Credentials like the Google Professional Data Engineer, AWS Certified Data Engineer – Associate, and Databricks certifications validate your expertise and can help justify a higher salary.

• Contribute to Open-Source Data Tools or Lead Architectural Redesigns: Public contributions showcase your skills to the broader community and can increase your visibility with recruiters. Leading system overhauls demonstrate high-value leadership and technical vision.

• Take On Roles Involving ML Data Prep or Cross-Functional Platform Ownership: Supporting ML pipelines, enabling AI-driven analytics, or managing end-to-end data platforms increases your strategic impact—and your earning potential.

Conclusion

Big data engineers play a pivotal role in turning massive, complex datasets into usable insights that guide business decisions. As companies continue to invest in data-driven strategies, the demand and pay for skilled professionals in this field remain strong. 

Salaries vary based on factors like location, industry, and specialization, but with the right skills, certifications, and experience, one can reach well into the six-figure range. The key to staying competitive is keeping up with emerging tech and learning new skills. 

If you're ready to boost your skills, check out all our data analytics online courses.