Big Data Engineer
GrowingBuilding pipelines and processing systems for data at petabyte-scale volumes
Big Data Engineer Salary
United Kingdom
£55,000 – £95,000
United States
$100,000 – $165,000
Varies by location, sector, and experience. Last updated: 2025.
What Is a Big Data Engineer?
A Big Data Engineer builds and maintains the data pipelines, processing systems, and infrastructure that handle data at volumes, velocities, or varieties that conventional database tools cannot manage effectively. The role is a specialisation of Data Engineering — focused specifically on distributed computing frameworks, large-scale streaming and batch processing, and the cloud data platforms designed for petabyte-scale workloads.
In practice the boundary between Data Engineer and Big Data Engineer has narrowed considerably as cloud-native platforms have made distributed processing more accessible. The distinction matters most in organisations processing genuinely massive data volumes — real-time event streams at millions of events per second, multi-petabyte data lakes, or complex multi-source analytical pipelines requiring distributed compute. Big Data Engineers work primarily with Apache Spark, Kafka, and cloud-native equivalents, alongside lakehouse platforms such as Databricks and Snowflake. Python and SQL remain the primary development languages, with Scala still relevant in Spark-heavy environments.
What Does a Big Data Engineer Do?
- Build and maintain distributed data processing pipelines using Apache Spark or cloud-native equivalents at petabyte scale
- Design and implement streaming data pipelines using Kafka or Kinesis to support real-time ingestion and event-driven processing
- Develop and optimise data lake and lakehouse architectures using Delta Lake or Apache Iceberg for reliable large-scale storage
- Manage pipeline orchestration using Airflow, Prefect, or Dagster, scheduling jobs and handling failure recovery systematically
- Implement data quality checks and monitoring across large-scale pipelines to detect and surface issues before downstream consumption
- Collaborate with data architects, ML engineers, and analytics engineers on platform design, data modelling standards, and infrastructure decisions
Big Data Engineer Skills
Hard Skills
- Distributed Computing
- Python
- SQL
- Data Pipeline Orchestration
- ETL Pipeline Design
- Event Streaming Architecture
- Data Lake Architecture
- Apache Spark Optimisation
- Scala
- Cluster Architecture
- Performance Optimisation
Soft Skills
- Engineering DisciplineBuilding reliable distributed pipelines at scale
- Analytical ThinkingDesigning processing architecture for data volume and velocity
- Problem SolvingDiagnosing failures across Spark, Kafka, and cloud platforms
- CollaborationWorking across data science, analytics, and platform teams
- PragmatismChoosing appropriate tools for scale rather than novelty
- Attention to DetailData quality and schema consistency across pipelines
Big Data Engineer Tools & Software
How to Become a Big Data Engineer
Typical Route
Most Big Data Engineers hold a bachelor's degree in Computer Science, Software Engineering, or Mathematics. ABET-accredited programs at institutions including the University of California Berkeley, the University of Illinois Urbana-Champaign, and Georgia Tech provide strong data systems and distributed computing foundations. An MSc in Data Science or Computer Science, available at institutions including Carnegie Mellon University and MIT, supports specialist progression, though strong Python and SQL skills alongside hands-on experience carry more weight at hiring stage.
Alternative Route
Databricks Certified Associate Developer for Apache Spark and Data Engineer Professional are the most directly relevant credentials; Databricks Academy delivers structured preparation. AWS Data Analytics Specialty, Google Professional Data Engineer, and Microsoft Azure Data Engineer Associate support cloud platform specialisation; A Cloud Guru and Pluralsight offer preparation courses. The Data Engineering Zoomcamp from DataTalks.Club is a free structured learning programme widely used by US practitioners. A GitHub portfolio of working Spark jobs and streaming pipelines is the primary entry signal.
A Day in the Life of a Big Data Engineer
Morning
Afternoon
Big Data Engineer Career Path
Data Engineer / Software Engineer
0–3 yrs
Big Data Engineer
3–6 yrs
Senior Big Data Engineer
6–9 yrs
Staff Engineer / Big Data Architect
9+ yrs
Principal Engineer / Head of Data Engineering
12+ yrs
Big Data Engineer Job Outlook
Big Data Engineering is in growing demand as organisations build out data platform capabilities to support AI, real-time analytics, and data products at scale. Databricks and Spark expertise are among the most consistently valued data engineering skills in the market. Engineers who combine strong Spark optimisation skills with lakehouse architecture knowledge and streaming pipeline experience are in the strongest position.