Job description template
Data Engineer Job Description
Find top Data Engineering talent with this job description template. Covers pipeline development, cloud platforms, required technical skills, and salary.
The role in brief
A Data Engineer designs, builds, and maintains the data infrastructure and pipelines that enable organizations to collect, store, transform, and serve data at scale. Unlike Data Analysts or Data Scientists who focus on interpreting data, Data Engineers focus on making data accessible, reliable, and performant. They architect data warehouses and data lakes, build ETL/ELT pipelines, and ensure data quality and governance across the organization. This role requires deep expertise in distributed systems, SQL, programming, and cloud platforms, combined with the ability to design systems that handle terabytes of data reliably.
- Design, build, and maintain scalable ETL/ELT pipelines that ingest data from diverse sources including APIs, databases, streaming platforms, and third-party services
- Architect and optimize data warehouse and data lake solutions on cloud platforms (AWS Redshift, Google BigQuery, Snowflake, Databricks)
- Implement data quality frameworks including validation rules, monitoring, anomaly detection, and alerting for pipeline failures
- Develop and maintain data models that support analytical and operational workloads while balancing query performance with storage efficiency
Paste the description into Cohesyve and it generates a Data Engineer assessment with a scoring rubric. Ten candidates free, no card.
Responsibilities
- Design, build, and maintain scalable ETL/ELT pipelines that ingest data from diverse sources including APIs, databases, streaming platforms, and third-party services
- Architect and optimize data warehouse and data lake solutions on cloud platforms (AWS Redshift, Google BigQuery, Snowflake, Databricks)
- Implement data quality frameworks including validation rules, monitoring, anomaly detection, and alerting for pipeline failures
- Develop and maintain data models that support analytical and operational workloads while balancing query performance with storage efficiency
- Build real-time and batch data processing systems using Apache Spark, Kafka, Airflow, or similar distributed computing frameworks
- Collaborate with Data Scientists and Analysts to understand data requirements and deliver clean, documented, and well-modeled datasets
- Implement data governance practices including access controls, data lineage tracking, PII handling, and compliance with privacy regulations
- Optimize query performance and database operations through indexing strategies, partitioning, and materialized views
- Manage infrastructure as code using Terraform, CloudFormation, or Pulumi and maintain CI/CD pipelines for data platform deployments
- Create and maintain comprehensive documentation for data schemas, pipeline architectures, and operational runbooks
Required skills
Cohesyve
Test these skills before the Data Engineer interviews
Cohesyve reads the description above and generates a role-specific assessment with a scoring rubric. Each candidate gets a different version, so questions cannot be shared.
Nice to have
Qualifications
- 1Bachelor's or Master's degree in Computer Science, Software Engineering, Data Science, or related technical field
- 23-6 years of experience in data engineering, software engineering, or database administration
- 3Hands-on experience with at least one modern data warehouse platform (Snowflake, BigQuery, Redshift, Databricks)
- 4Proven experience building production data pipelines that process millions of records daily
- 5Strong understanding of software engineering best practices including testing, code review, and documentation
Compensation and environment
Salary range
Work environment
Career growth
Data Engineers progress to Senior Data Engineer, Staff Data Engineer, Data Platform Lead, or Data Engineering Manager. Specialized paths include Analytics Engineering, ML Engineering, or Data Architecture roles. Many move into engineering management or VP of Data positions.
Common questions
What is the difference between a Data Engineer and a Data Scientist?
Data Engineers focus on building and maintaining the infrastructure and pipelines that make data available and reliable. Data Scientists use that infrastructure to perform statistical analysis, build predictive models, and extract insights. Think of Data Engineers as building the roads and Data Scientists as driving on them to reach conclusions.
Which programming language is most important for Data Engineers?
Python is the most widely used language in data engineering due to its ecosystem of data libraries and its role as the primary language for tools like Airflow, dbt, and Spark. SQL is equally essential for data manipulation and warehouse operations. Scala is valuable for organizations heavily invested in the Apache Spark ecosystem.
Is a Master's degree required for Data Engineering?
A Master's degree is not required for most data engineering positions. A bachelor's degree in computer science or a related field combined with relevant experience is typically sufficient. Many successful Data Engineers come from software engineering backgrounds and transitioned by gaining cloud platform and data pipeline experience.
What cloud certifications are valuable for Data Engineers?
The most respected certifications include AWS Certified Data Analytics Specialty, Google Professional Data Engineer, Azure Data Engineer Associate, and Databricks Certified Data Engineer. These certifications validate platform-specific skills and are increasingly valued by employers, though practical experience and portfolio projects carry more weight in interviews.
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Cohesyve turns a job description into a role-specific assessment with a scoring rubric. Each candidate gets a different version, so questions cannot be shared between applicants.
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