What Does a Big Data Engineer Do?
A big data engineer builds and maintains systems that manage vast amounts of structured and unstructured data. Their primary role is to design scalable data pipelines that enable companies to collect, store, and process large datasets efficiently. These pipelines power analytics, machine learning models, and operational systems across the organization.
Working closely with data scientists, analysts, and IT infrastructure teams, big data engineers choose the right tools, optimize performance, and ensure data quality and reliability. They play a key role in turning raw data into usable assets, often working with technologies like Hadoop, Spark, Kafka, and cloud data services.
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Big Data Engineer Core Responsibilities
- Design, develop, and manage large-scale data processing pipelines
- Implement ETL/ELT processes for structured and unstructured data
- Optimize data storage solutions for speed, scalability, and cost
- Integrate data from various sources, including APIs, logs, and databases
- Collaborate with data scientists to provide clean, well-structured datasets
- Monitor pipeline performance and troubleshoot data flow issues
- Ensure data integrity, governance, and security compliance
- Work with cloud platforms (AWS, GCP, Azure) to manage data infrastructure
Required Skills and Qualifications
Hard skills
- Proficiency with big data frameworks like Hadoop, Spark, Flink, or Beam
- Experience with distributed systems and cloud data services (e.g., Amazon EMR, Google BigQuery, Azure Data Lake)
- Strong SQL skills and experience with NoSQL databases (MongoDB, Cassandra)
- Familiarity with stream processing tools (Apache Kafka, Kinesis)
- Programming experience in Python, Scala, or Java
- Understanding of data modeling, warehousing, and performance optimization
Soft skills
- Problem-solving and troubleshooting mindset
- Clear written and verbal communication, especially when working cross-functionally
- Strong attention to detail and commitment to data accuracy
- Project management and the ability to prioritize tasks in a fast-paced environment
Educational requirements
- Bachelor’s degree in computer science, information systems, or a related field
Certifications
- Google Cloud Professional Data Engineer, AWS Certified Data Analytics – Specialty, or Cloudera Certified Professional: Data Engineer (optional but preferred)
Preferred Qualifications
- 3+ years of experience working in a big data environment
- Familiarity with containerization (Docker, Kubernetes)
- Experience with CI/CD and DevOps practices in data engineering
- Prior work in regulated industries (healthcare, finance) with a focus on data compliance
National Average Salary
Big data engineer salaries vary by experience, industry, organization size, and geography. Click below to explore salaries by local market.
The average national salary for a Big Data Engineer is:
$146,950
Sample Job Description Templates for Big Data Engineers
Junior Big Data Engineer
Position Overview
The Junior Big Data Engineer supports the development and maintenance of scalable data pipelines and processing systems. This role is ideal for candidates early in their careers who want to build foundational skills in data engineering and cloud technologies.
Junior Big Data Engineer Responsibilities
- Assist with building and maintaining batch and real-time data pipelines
- Perform data cleansing, validation, and transformation tasks
- Support integration of external data sources into existing systems
- Collaborate with senior engineers on data lake and warehouse development
- Monitor system performance and report data issues
Junior Big Data Engineer Requirements
Hard skills
- Proficiency in SQL and Python
- Familiarity with big data tools such as Hadoop or Spark
- Experience using cloud platforms (AWS, GCP, or Azure)
- Understanding of data modeling concepts
Soft skills
- Eagerness to learn and take direction
- Attention to detail
- Effective communication and teamwork
Educational requirements
- Bachelor’s degree in computer science, information systems, or a related field
Preferred Qualifications
- Internship or coursework in data engineering or data science
- Familiarity with Git and version control systems
- Exposure to Apache Kafka or stream processing tools
Senior Big Data Engineer
Position Overview
The Senior Big Data Engineer designs and implements scalable data processing pipelines and infrastructure. This role requires advanced experience with big data tools and the ability to independently lead complex projects and mentor junior staff.
Senior Big Data Engineer Responsibilities
- Develop large-scale ETL/ELT pipelines using Spark, Kafka, or Flink
- Optimize performance and reliability of data workflows
- Lead architecture reviews and recommend improvements
- Ensure compliance with data governance and security standards
- Mentor junior engineers and participate in code reviews
Senior Big Data Engineer Requirements
Hard skills
- Deep expertise in big data tools (Spark, Hadoop, Hive, Airflow)
- Strong programming in Python, Scala, or Java
- Experience with cloud-based data platforms (AWS Glue, Google Dataflow)
- Strong SQL and NoSQL database experience
Soft skills
- Strong analytical and troubleshooting skills
- Ability to work independently and lead projects
- Excellent communication and collaboration skills
Educational requirements
- Bachelor’s degree in a relevant technical field
Preferred Qualifications
- Master’s degree in data engineering or computer science
- Certifications like GCP Professional Data Engineer or AWS Certified Data Analytics
- Experience with container orchestration (Docker, Kubernetes)
Lead Data Engineer
Position Overview
The Lead Data Engineer is responsible for the technical direction and execution of data engineering projects. This role involves overseeing architecture, managing engineering teams, and aligning data systems with organizational goals.
Lead Data Engineer Responsibilities
- Lead the design and delivery of robust data architecture and pipelines
- Manage a team of data engineers and assign development tasks
- Define best practices for data quality, storage, and access
- Collaborate with product and analytics teams to define data requirements
- Oversee cloud migration or modernization initiatives
Lead Data Engineer Requirements
Hard skills
- Expertise in distributed systems and large-scale data processing
- Advanced knowledge of big data tools and cloud infrastructure
- Proven experience managing CI/CD workflows and DevOps practices
Soft skills
- Strong leadership and team-building abilities
- Strategic thinking with the ability to manage priorities
- Clear, effective communicator with cross-functional teams
Educational requirements
- Bachelor’s degree in a technical field; master’s preferred
Preferred Qualifications
- Experience managing engineering teams or scrum teams
- Certifications in cloud architecture or data engineering
- Familiarity with real-time analytics and edge data pipelines
Principal Data Engineer
Position Overview
The Principal Data Engineer defines enterprise-level data architecture and long-term data strategy. They lead innovation efforts and ensure all systems support analytics, compliance, and scalability needs across the business.
Principal Data Engineer Responsibilities
- Architect and evolve end-to-end data platforms for global scale
- Set technical standards and enforce engineering best practices
- Evaluate and implement emerging technologies
- Collaborate with executives on strategic data initiatives
- Oversee data lifecycle management, lineage, and metadata strategy
Principal Data Engineer Requirements
Hard skills
- Extensive experience in data architecture, governance, and security
- Mastery of Spark, Kafka, Flink, and cloud-native data solutions
- Proven track record designing enterprise-scale systems
Soft skills
- Visionary thinking and technical leadership
- Excellent stakeholder communication
- Ability to translate business goals into scalable engineering solutions
Educational requirements
- Bachelor’s or master’s degree in data engineering or computer science
Preferred Qualifications
- 10+ years of experience in data engineering
- Publications or patents in data systems or AI infrastructure
- Enterprise cloud certification (e.g., GCP Cloud Architect, AWS Solutions Architect)
Streaming Data Engineer
Position Overview
The Streaming Data Engineer builds and maintains real-time data pipelines and architectures. This role focuses on event-driven systems, low-latency processing, and immediate data availability for analytics and operations.
Streaming Data Engineer Responsibilities
- Develop real-time data pipelines using Kafka, Flink, or Kinesis
- Implement event-driven architecture and streaming ETL processes
- Ensure low-latency, high-throughput data delivery
- Troubleshoot and optimize message queues and stream jobs
- Collaborate with product teams to support real-time use cases
Streaming Data Engineer Requirements
Hard skills
- Proficiency in Apache Kafka, Apache Flink, Spark Streaming, or AWS Kinesis
- Experience with real-time event processing and message brokers
- Strong programming in Java, Scala, or Python
Soft skills
- Ability to manage fast-paced, high-volume environments
- Attention to detail and performance metrics
- Strong communication and documentation skills
Educational requirements
- Bachelor’s degree in a technical discipline
Preferred Qualifications
- Experience with schema registries and streaming data contracts
- Familiarity with Flink SQL or Kafka Streams
- Cloud streaming certifications (e.g., AWS Streaming Data Specialist)
Cloud Data Engineer
Position Overview
The Cloud Data Engineer develops and maintains cloud-native data pipelines and platforms. They are responsible for cloud architecture, orchestration, and performance optimization across large datasets.
Cloud Data Engineer Responsibilities
- Design and deploy scalable data infrastructure in AWS, Azure, or GCP
- Build serverless or containerized ETL/ELT pipelines
- Integrate cloud storage, compute, and analytics services
- Automate infrastructure with Terraform, CloudFormation, or Deployment Manager
- Monitor and tune systems for cost efficiency and performance
Cloud Data Engineer Requirements
Hard skills
- Experience with GCP BigQuery, AWS Redshift, or Azure Synapse
- Proficiency in cloud-native tools like Glue, Dataflow, Databricks, or Snowflake
- Knowledge of IaC tools and pipeline orchestration (Airflow, Step Functions)
Soft skills
- Strong problem-solving and system design capabilities
- Ability to work cross-functionally in agile teams
- Excellent time management and communication
Educational requirements
- Bachelor’s degree in computer science or cloud computing
Preferred Qualifications
- Cloud certification (AWS Data Analytics, GCP Data Engineer, or Azure Data Engineer)
- Experience with hybrid or multi-cloud architectures
- Background in data governance and security in cloud environments