What Does a Data Engineer Do?
A data engineer designs and builds the infrastructure that allows organizations to collect, move, and store data at scale. They create and maintain robust pipelines that transform raw data into structured, accessible formats for analytics, machine learning, and operational systems. From real-time ingestion to batch processing, their work ensures that data is accurate, timely, and available where it’s needed most.
Data engineers work closely with data analysts, scientists, software engineers, and architects to integrate systems, automate workflows, and optimize data performance. They’re often responsible for setting up data lakes and warehouses, writing transformation logic, and monitoring the quality and efficiency of data flows across platforms and tools. Their work is foundational to any successful data strategy.
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Data Engineer Core Responsibilities
- Design and build scalable ETL/ELT pipelines for structured and unstructured data
- Develop and maintain batch and real-time data ingestion systems
- Create and manage data models, schemas, and transformation logic
- Optimize data flows for performance, reliability, and cost-efficiency
- Collaborate with data analysts, scientists, and product teams to understand data needs
- Implement monitoring and alerting for data quality and pipeline performance
- Automate workflows using orchestration tools and version control systems
- Maintain compliance with data governance, privacy, and security standards
Required Skills and Qualifications
Hard skills
- Proficiency in Python, SQL, and data pipeline development
- Experience with ETL/ELT tools (e.g., Airflow, dbt, Informatica, Talend)
- Familiarity with big data processing tools like Apache Spark or Kafka
- Working knowledge of cloud platforms (e.g., AWS, Azure, GCP) and data storage services
- Experience with data modeling, schema design, and API integration
Soft skills
- Strong problem-solving and systems thinking
- Ability to work across technical teams and disciplines
- Clear communication and documentation practices
- Attention to detail and commitment to data accuracy
Education
- Bachelor’s degree in computer science, data engineering, or a related technical field
Certifications
None required, but recommended certifications include Google Cloud Professional Data Engineer, AWS Certified Data Analytics – Specialty, and Microsoft Certified: Azure Data Engineer Associate.
Preferred Qualifications
- Experience building cloud-native data platforms
- Familiarity with containerization and CI/CD workflows
- Exposure to data lakehouse or real-time analytics environments
- Background in regulated industries with data compliance needs
National Average Salary
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 Data Engineer is:
$113,965
Sample Job Description Templates for Data Engineers
Junior Data Engineer
Position Overview
A junior data engineer supports the development and maintenance of data systems under the guidance of senior team members. This role involves assisting with data ingestion, transformation, and basic pipeline monitoring to ensure smooth data flow across the organization.
Responsibilities
- Support ETL/ELT workflows and maintain data pipeline reliability
- Write and troubleshoot basic SQL queries and transformation scripts
- Assist with data quality checks and documentation
- Collaborate with analysts and developers on data needs and fixes
- Learn and apply data engineering best practices in a team setting
Requirements
Hard skills
- Foundational knowledge of SQL and Python
- Exposure to data integration or ETL tools (e.g., Airflow, dbt)
- Familiarity with relational databases and basic cloud concepts
Soft skills
- Willingness to learn and ask questions
- Detail-oriented and reliable with assigned tasks
- Collaborative and open to feedback
Education
- Bachelor’s degree in computer science, data analytics, or related field
Certifications
- None required; Google Data Analytics or AWS Cloud Practitioner (recommended)
Preferred Qualifications
- Internship or coursework involving data pipelines or databases
- Basic Git or version control knowledge
Data Engineer
Position Overview
A data engineer develops and maintains scalable pipelines to collect, transform, and store data used across analytics, operations, and applications. They work independently to implement clean, efficient solutions that align with business goals.
Responsibilities
- Build and manage data pipelines for batch or real-time processing
- Develop transformation logic using SQL, Python, or ETL tools
- Collaborate with analysts, scientists, and engineers to meet data needs
- Monitor pipeline health and resolve failures or latency issues
- Write documentation and contribute to schema and model design
Requirements
Hard skills
- Proficiency in SQL and Python
- Experience with ETL platforms (e.g., Airflow, dbt, Informatica)
- Familiarity with cloud data environments (AWS, GCP, or Azure)
Soft skills
- Problem-solving and independent ownership of tasks
- Effective cross-functional communication
- Time management across multiple projects
Education
- Bachelor’s degree in a technical field
Certifications
- Google Cloud Professional Data Engineer or AWS Data Analytics (recommended)
Preferred Qualifications
- Experience with orchestration tools and version-controlled deployment
- Exposure to data lake or warehouse platforms (e.g., Redshift, Snowflake)
Senior Data Engineer
Position Overview
A senior data engineer leads the design and optimization of advanced data infrastructure. They own complex pipelines, enforce best practices, and mentor junior engineers to build scalable and reliable systems.
Responsibilities
- Design high-performance, scalable data pipelines for cross-functional use
- Refactor existing pipelines for reliability and cost-efficiency
- Drive standardization in data transformation and modeling
- Collaborate on architecture decisions with engineers and product stakeholders
- Mentor junior engineers and perform code reviews
- Ensure data security, governance, and compliance are implemented
Requirements
Hard skills
- Advanced SQL and Python for complex data processing
- Deep experience with big data tools (e.g., Spark, Kafka, Snowflake)
- Cloud-native development experience (e.g., AWS Glue, GCP Dataflow)
Soft skills
- Leadership and team mentorship
- Strong communication across technical and non-technical teams
- Strategic thinking in system planning and scaling
Education
- Bachelor’s or master’s degree in computer science or data engineering
Certifications
- Cloud platform certifications strongly preferred (AWS, GCP, Azure)
Preferred Qualifications
- Experience building platform services or reusable components
- Familiarity with CI/CD workflows and testing for data pipelines
Lead Data Engineer
Position Overview
A lead data engineer sets technical direction for a team or domain, overseeing the planning and execution of complex data projects. They guide architecture decisions, mentor engineers, and align engineering efforts with business outcomes.
Responsibilities
- Lead design of robust, scalable data solutions across teams
- Coordinate technical decisions, implementation plans, and project timelines
- Act as a subject matter expert for data infrastructure and platform architecture
- Mentor and coach engineers to build skills and improve code quality
- Evaluate new tools and frameworks for organizational adoption
- Collaborate with product and engineering leadership to prioritize work
Requirements
Hard skills
- Expertise in distributed data processing and pipeline orchestration
- Deep cloud infrastructure experience and platform automation
- Strong architectural judgment for data models and workflows
Soft skills
- Technical leadership and stakeholder influence
- Conflict resolution and consensus-building
- Strong planning and execution accountability
Education
- Bachelor’s or master’s degree in a technical field
Certifications
- Cloud data architect certifications (AWS, Azure, or GCP) recommended
Preferred Qualifications
- History of leading large data migrations or platform builds
- Experience creating team-wide engineering standards and practices
Principal Data Engineer
Position Overview
A principal data engineer shapes data strategy and architecture at an enterprise level. They provide technical leadership across multiple teams, influence engineering standards, and guide the evolution of scalable, modern data platforms.
Responsibilities
- Define long-term data architecture vision across business units
- Lead high-impact cross-team initiatives involving complex data workflows
- Establish platform-level standards for data modeling, ingestion, and governance
- Partner with leadership on infrastructure strategy, budgeting, and tool adoption
- Mentor senior and lead engineers and support org-wide growth initiatives
- Evaluate cutting-edge data technologies and lead adoption planning
Requirements
Hard skills
- Mastery of cloud-native, distributed data infrastructure
- Strong background in systems architecture and platform scaling
- Broad tool fluency (e.g., Spark, Airflow, Snowflake, Kubernetes, Terraform)
Soft skills
- Executive communication and organizational influence
- Visionary thinking balanced with delivery execution
- Leadership across multiple teams and stakeholders
Education
- Bachelor’s or master’s degree in computer science or engineering
Certifications
- Multiple relevant cloud/data certifications expected at this level
Preferred Qualifications
- Experience building and evolving enterprise-scale data platforms
- Thought leadership in the data engineering community (publishing, speaking, mentoring)