Data Engineer Job Description Template - 2025 Guide

A Data Engineer builds and maintains the infrastructure that enables organizations to collect, store, process, and analyze data at scale. They design robust data pipelines, implement ETL processes, and ensure data quality and accessibility for analytics teams and business stakeholders.

Key Highlights

• Average Salary Range: $95,000 - $170,000 annually in the United States
• Core Focus: Data pipeline architecture, ETL development, and infrastructure management
• Growth Trajectory: High demand with 35% projected job growth through 2032
• Technical Stack: SQL, Python, Spark, Kafka, AWS/Azure/GCP cloud platforms
• Impact Area: Enables data-driven decision making across entire organization
• Remote Flexibility: 70% of positions offer remote or hybrid work arrangements

Why This Role Matters

Data Engineers serve as the backbone of modern data-driven organizations, creating the foundation that enables analysts, data scientists, and business leaders to extract meaningful insights from raw data. As companies increasingly rely on data for competitive advantage, Data Engineers ensure information flows efficiently from various sources to end users while maintaining quality, security, and performance standards.

The role combines software engineering principles with deep understanding of data systems, making it essential for organizations looking to scale their analytics capabilities and implement advanced AI/ML initiatives.

Primary Job Description Template

About the Role

We are seeking a skilled Data Engineer to join our growing data team and drive the development of our data infrastructure. You will design, build, and maintain scalable data pipelines that power our analytics ecosystem, working closely with data scientists, analysts, and product teams to ensure reliable access to high-quality data.

In this role, you will architect solutions that handle diverse data sources, implement robust ETL processes, and optimize data workflows for performance and cost efficiency. You will contribute to our data platform strategy while ensuring compliance with security and governance standards.

This position reports to the Senior Data Engineering Manager and collaborates extensively with cross-functional teams including Analytics, Product, and Engineering to deliver data solutions that drive business impact.

Key Responsibilities

• Design and implement scalable data pipelines using modern ETL/ELT frameworks and cloud technologies
• Develop and maintain data warehouse schemas, data lakes, and real-time streaming architectures
• Build automated data quality monitoring systems and implement data validation frameworks
• Optimize database performance, query efficiency, and storage costs across cloud platforms
• Collaborate with data scientists to productionize machine learning models and feature stores
• Implement data governance policies, security controls, and compliance monitoring systems
• Create and maintain comprehensive documentation for data systems and processes
• Monitor and troubleshoot data pipeline failures, ensuring minimal downtime and data loss
• Evaluate and integrate new data technologies and tools to improve team productivity
• Mentor junior engineers and contribute to technical architecture decisions

Requirements

Must-Have Qualifications:

• Bachelor's degree in Computer Science, Data Engineering, or related technical field
• 3+ years of experience in data engineering, software development, or related roles
• Strong proficiency in SQL and at least one programming language (Python, Scala, or Java)
• Experience with cloud platforms (AWS, Azure, or GCP) and their data services
• Hands-on experience with data pipeline orchestration tools (Airflow, Luigi, or similar)
• Knowledge of data warehousing concepts, dimensional modeling, and data lake architectures
• Experience with distributed computing frameworks (Spark, Hadoop, or similar)
• Understanding of version control systems, CI/CD practices, and software development lifecycle

Nice-to-Have Qualifications:

• Master's degree in Data Engineering, Computer Science, or quantitative field
• Experience with real-time streaming technologies (Kafka, Kinesis, Pub/Sub)
• Knowledge of containerization and orchestration technologies (Docker, Kubernetes)
• Familiarity with Infrastructure as Code tools (Terraform, CloudFormation)
• Experience with data visualization tools and business intelligence platforms

What We Offer

• Competitive Compensation: Base salary range of $110,000 - $150,000 plus equity participation
• Comprehensive Benefits: Health, dental, vision insurance with company-paid premiums
• Professional Development: $3,000 annual learning budget and conference attendance support
• Flexible Work Environment: Remote-first culture with optional office access
• Technology Stipend: $2,000 annual allowance for home office setup and equipment
• Growth Opportunities: Clear career progression paths and technical leadership opportunities

Context Variations

Corporate Environment

In large enterprise settings, Data Engineers focus heavily on governance, compliance, and integration with legacy systems. Emphasis on security protocols, data lineage tracking, and collaboration with multiple business units requiring standardized data models and reporting frameworks.

Startup Environment

Startup Data Engineers wear multiple hats, often handling both infrastructure and analytics responsibilities. Focus on rapid prototyping, cost optimization, and building MVP data solutions that can scale. Direct collaboration with founders and product teams with emphasis on speed and flexibility over extensive documentation.

Remote/Hybrid Environment

Remote Data Engineers must excel at asynchronous communication and self-directed work. Strong documentation skills and proactive communication about pipeline status and issues are essential. Regular video check-ins with team members and clear escalation procedures for critical data failures.

Industry Considerations

Industry	Key Requirements	Compliance Needs
Financial Services	Real-time fraud detection, high-frequency trading data, regulatory reporting	SOX, PCI-DSS, Basel III
Healthcare	HIPAA-compliant architectures, clinical data integration, research datasets	HIPAA, FDA 21 CFR Part 11
E-commerce	Customer behavior tracking, inventory management, recommendation engines	GDPR, CCPA, PCI-DSS
Technology	Product usage analytics, A/B testing infrastructure, user engagement metrics	GDPR, SOC 2, ISO 27001
Manufacturing	IoT sensor data, supply chain optimization, predictive maintenance	ISO 9001, FDA (for regulated products)
Media & Entertainment	Content performance analytics, user engagement, ad serving optimization	COPPA, GDPR, broadcast regulations

Compensation Guide

Salary Information

National Average Range: $95,000 - $170,000 annually

Experience-Based Breakdown:

• Entry Level (0-2 years): $85,000 - $115,000
• Mid-Level (3-5 years): $110,000 - $145,000
• Senior Level (6+ years): $140,000 - $190,000
• Staff/Principal Level: $180,000 - $250,000+

Geographic Salary Variations

Metropolitan Area	Salary Range	Cost of Living Factor
San Francisco Bay Area	$130,000 - $220,000	High demand, tech concentration
New York City	$120,000 - $200,000	Financial services premium
Seattle	$115,000 - $185,000	Tech hub, cloud provider presence
Austin	$105,000 - $165,000	Growing tech scene, lower COL
Chicago	$100,000 - $160,000	Financial and healthcare industries
Denver	$95,000 - $155,000	Emerging tech market
Remote	$90,000 - $170,000	Varies by company location/policy

Factors Affecting Compensation:

• Cloud platform certifications (AWS, Azure, GCP) can add 10-15% premium
• Advanced degree or specialized skills (ML Engineering, Real-time systems) increase range
• Industry vertical (Finance, Healthcare) may offer higher compensation

Salary data compiled from Glassdoor, PayScale, and industry surveys as of January 2025

Interview Questions

Technical/Functional Questions

1. Pipeline Architecture: "Design a data pipeline to process 10TB of daily log data. Walk me through your architecture decisions, technology choices, and scalability considerations."

2. Data Modeling: "Explain the difference between star schema and snowflake schema. When would you choose each approach for a data warehouse?"

3. Performance Optimization: "A critical data pipeline is running slowly and missing SLA deadlines. How would you diagnose and resolve performance bottlenecks?"

4. Data Quality: "Describe your approach to implementing data quality checks in a streaming data pipeline. How would you handle schema evolution?"

5. Cloud Technologies: "Compare and contrast data lake vs. data warehouse architectures. When would you recommend each approach?"

6. Real-time Processing: "Explain the lambda architecture pattern. What are its advantages and disadvantages compared to kappa architecture?"

7. Distributed Systems: "How does data partitioning work in Apache Spark? Describe scenarios where you'd use different partitioning strategies."

8. Monitoring & Alerting: "What metrics would you track for a critical ETL pipeline? How would you set up alerting for data freshness and quality issues?"

Behavioral Questions

1. Problem-Solving: "Tell me about a time when a data pipeline failed in production. How did you handle the incident and prevent future occurrences?"

2. Collaboration: "Describe a situation where you had to work with stakeholders who had conflicting data requirements. How did you resolve the situation?"

3. Technical Leadership: "Share an example of when you introduced a new technology or approach to your data engineering team. How did you gain buy-in?"

4. Priority Management: "Tell me about a time when you had to balance multiple urgent data requests. How did you prioritize and communicate with stakeholders?"

5. Continuous Learning: "Describe how you stay current with evolving data technologies. Give an example of a new tool you recently learned and applied."

Culture Fit Questions

1. Data-Driven Mindset: "How do you approach making technical decisions when building data systems? What factors do you consider?"

2. Quality Standards: "What does 'good' data engineering look like to you? How do you ensure quality in your work?"

3. Team Collaboration: "How do you prefer to work with data scientists and analysts? Describe your ideal collaboration process."

4. Innovation Balance: "How do you balance using proven technologies versus experimenting with new tools in production systems?"

Evaluation Tips: Look for candidates who demonstrate both technical depth and practical experience. Strong candidates will discuss trade-offs, mention specific technologies, and show understanding of business impact. Pay attention to how they approach problem-solving and their ability to communicate complex technical concepts clearly.

Hiring Tips

Quick Sourcing Guide

Top Sourcing Platforms:

• LinkedIn: Focus on professionals with "Data Engineer," "ETL Developer," or "Big Data" in titles
• GitHub: Search for repositories with data pipeline code, especially Apache Airflow DAGs
• Stack Overflow: Target users active in data engineering tags (apache-spark, pandas, sql)
• AngelList: Strong for startup-focused data engineers comfortable with ambiguity

Professional Communities:

• Data Engineering Slack communities and local meetups
• Spark and Kafka user groups
• Cloud provider user groups (AWS, Azure, GCP data services)

Posting Optimization Tips:

• Highlight specific technologies in your stack (Spark, Kafka, Airflow)
• Mention data scale (TB/day, millions of records) to attract experienced candidates
• Include remote work options prominently in title/description
• Specify cloud platform to attract relevant experience

Red Flags to Avoid

• Only SQL Experience: Candidates without programming skills in Python/Scala/Java may struggle with modern data engineering
• No Cloud Experience: Limited understanding of cloud data services indicates outdated skill set
• Siloed Approach: Reluctance to collaborate with data scientists and analysts suggests poor cultural fit
• No Production Experience: Lack of experience with system monitoring, debugging, and incident response
• Buzzword Heavy: Overuse of technical terms without demonstrating practical application
• No Version Control: Unfamiliarity with Git and collaborative development practices

FAQ Section