A data engineer is the architect and builder of an organization’s data infrastructure that collects, stores, and processes large volumes of data. They design data pipelines, build ETL/ELT processes, optimize database performance, and ensure data quality across systems.
Writing an effective data engineer job description helps you attract candidates with the right technical skills and experience level. A clear job post reduces time spent reviewing unqualified applications and ensures you find engineers who can handle your specific data challenges.
In this guide, you’ll explore customizable data engineer job description templates for all experience levels, plus specific requirements and responsibilities for each role. Let’s get started!
First, let’s examine a comprehensive template you can adapt for your hiring needs. This data engineer job description template covers all sections that top candidates expect to see:
Each section includes placeholders marked with {{ }} brackets to help you customize the content for your technology stack. You can modify these elements to match your company’s setup and project requirements.
Job title: {{ Junior / Middle / Senior }} Data Engineer
Location: {{ Remote / Hybrid / On-site – City, Country }}
Salary range: {{ X,000-Y,000 }} per year {{ gross / net }}
Employment type: {{ Full-time / Part-time / Contract }}
{{ Company name }} is looking for a Data Engineer to build, maintain, and optimize our data infrastructure. You’ll work with a wide variety of data sources, including databases and large data sets using Python and {{ SQL / AWS / Spark / other relevant technologies }}.
You’ll join our data engineering team and collaborate closely with {{ data science / analytics / backend }} specialists to provide clean, reliable data for analysis and decision-making.
Your daily work will involve designing and maintaining systems that handle our data from collection to analysis. Here’s what you’ll focus on:
â–ª Develop and maintain data pipelines for efficient data {{ collection / extraction / processing }}
â–ª Design and optimize data storage solutions
â–ª Implement ETL/ELT workflows to integrate data from multiple sources into our {{ data warehouse / data lake }}
â–ª Ensure data quality and integrity through {{ validation / cleansing / transformation / monitoring }} processes
â–ª Integrate data from various sources, including {{ your databases / APIs }}
â–ª Optimize data processing and queries for performance
â–ª Monitor data workflows and troubleshoot issues
The ideal candidate for this data engineering role should have:
â–ª {{ x }}+ years of professional experience in data engineering or a related field
â–ª Proven experience developing data pipelines or data integration solutions
â–ª Python programming skills and SQL proficiency
â–ª Solid understanding of data architecture and modeling, database design, and query optimization for large datasets
â–ª Experience with big data frameworks and distributed systems (e.g., {{ Hadoop / Hive / Apache Spark / Kafka / other }})
â–ª Experience with {{ AWS / Azure / GCP }} cloud-based data platforms and its services (e.g., {{ Redshift / BigQuery / Kafka / EMR }})
â–ª Experience with {{ Apache Airflow + dbt / Prefect / Dagster / other workflow orchestration tools }}
â–ª Knowledge of SQL and NoSQL databases, Snowflake
â–ª Proficiency in version control using Git and familiarity with CI/CD practices
â–ª Strong problem-solving and analytical skills
Additional experience that would make you stand out includes:
â–ª Familiarity with stream processing tools (e.g., Apache Flink, Kafka Streams)
â–ª Experience with Infrastructure as Code (e.g., Terraform)
â–ª Knowledge of data versioning tools (DVC, LakeFS)
â–ª Experience with data governance and cataloging tools
Join our team and enjoy these benefits to support your career growth:
â–ª Competitive salary with performance-based bonuses
â–ª Comprehensive health coverage
â–ª Flexible working arrangements with remote options
â–ª Professional development budget
â–ª Generous vacation policy and personal time off
â–ª Modern tech setup
Send your resume along with a brief cover letter highlighting your data engineering experience. Include links to relevant projects or GitHub repositories.
Our hiring process typically includes a technical screening, coding assessment, and final interview with the engineering team.
Explore our specialized job description guides for related roles:
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A junior data engineer is an entry-level professional who assists in building and maintaining data pipelines under senior guidance. It’s a learning-focused role that requires 1-2 years of commercial experience or a relevant internship. They typically focus on basic ETL development, cloud platform operations, foundational data processing tasks, and data quality practices.
Here’s what you can include in an entry-level data engineer job description:
Job title: Junior Data Engineer
Employment type: Full-time / Part-time
We’re seeking a motivated Junior Data Engineer to help build and maintain our data pipelines and ensure high-quality data delivery. You’ll work alongside experienced engineers who will mentor you as you develop your skills in data architecture and pipeline development.
This role offers hands-on experience with modern data tools and the opportunity to contribute to real projects. You’ll support our data team in creating reliable data pipelines and support data-driven initiatives across the organization.
As a junior team member, you’ll start with guided tasks and gradually take on more complex projects:
â–ª Assist in developing and maintaining data pipelines and ETL processes
â–ª Write scripts using Python and SQL to collect, clean, and load data from various sources
â–ª Perform data quality checks and help troubleshoot data inconsistencies
â–ª Monitor pipeline performance and report issues to senior engineers for quick resolution
â–ª Collaborate with senior data engineers to optimize data workflows and improve performance
â–ª Help manage and organize data in data warehouses or data lakes
â–ª Document data pipeline processes and contribute to best practices
â–ª Continuously learn new tools and technologies to improve your data engineering skills
We’re looking for someone with foundational technical skills and strong learning potential, including:
â–ª 1+ years of experience (or relevant internship/education) in data engineering or data analysis
â–ª Basic Python scripting and SQL querying skills
â–ª Understanding of relational databases (PostgreSQL, MySQL)
â–ª Familiarity with at least one cloud platform (AWS, Azure)
â–ª Knowledge of at least one ETL tool (Airflow, AWS Glue, or Azure Data Factory)
â–ª Understanding of data formats like CSV, JSON, Parquet
â–ª Basic knowledge of version control (Git)
â–ª Strong analytical and problem-solving abilities with high attention to detail
â–ª Good communication skills and a collaborative attitude, with eagerness to learn new technologies
Experience with these areas would be beneficial but not required:
â–ª Familiarity with big data tools like Apache Spark or Kafka
â–ª Awareness of NoSQL databases (MongoDB, Cassandra)
â–ª Familiarity with data visualization tools, such as Tableau, Power BI, or Looker
Now let’s examine what you can expect from senior-level candidates.
Obviously, a senior data engineer is a technical leader who designs multi-cloud data systems and makes strategic data architectural decisions. They have 5+ years of experience and deep expertise in cloud platforms, data processing frameworks, advanced SQL, and Python programming.
In addition to hands-on technical execution, they can lead data engineering teams and mentor junior engineers.
Here’s an example of a job description for a senior-level data engineer role:
Job title: Senior Data Engineer
Employment type: Full-time / Part-time / Contract
We’re looking for an experienced Senior Data Engineer to architect and scale our data infrastructure. You’ll lead technical decisions for our data platform and mentor junior data engineers.
This role requires someone who can evaluate new technologies and make architectural decisions. You’ll work closely with product and engineering leadership to guide the team’s technical direction.
As a senior team member, your data engineer job duties will include:
â–ª Architect and lead the development of large-scale data infrastructure
â–ª Design and implement distributed processing systems with Apache Spark or Flink
â–ª Oversee migration and integration of data platforms
â–ª Optimize pipelines for low-latency and high-throughput processing
â–ª Manage advanced data modeling, partitioning, and indexing strategies
â–ª Implement security and compliance frameworks for data assets
â–ª Mentor junior engineers and lead code reviews
â–ª Drive adoption of best practices in MLOps and advanced data analytics pipelines
â–ª Select, evaluate, and integrate new data technologies into the stack
We need someone with deep technical expertise and proven leadership experience, including:
â–ª 5+ years of experience in data engineering
â–ª Proven track record of architecting and implementing complex data systems
â–ª Advanced Python and SQL expertise; familiarity with additional languages (Java or Scala)
â–ª Deep experience with big data frameworks (Apache Spark, Apache Flink, Hadoop/Hive) and real-time streaming platforms (Kafka, Kinesis)
â–ª Experience with workflow orchestration (Airflow, Prefect, Dagster)
â–ª Experience with Infrastructure as Code tools (Terraform)
â–ª Strong experience with cloud platforms (AWS, Azure, or GCP) and their data services
â–ª Expertise in data lake and warehouse technologies (Snowflake, Delta Lake, Apache Iceberg)
â–ª Excellent problem-solving and system design skills
â–ª Proven leadership abilities
Additional expertise that would strengthen your application:
â–ª Expertise in data modeling for analytics and ML workloads
â–ª Experience in a specific domain (finance, e-commerce, retail, healthcare) with an understanding of industry-specific data challenges
â–ª Familiarity with advanced orchestration and workflow automation tools
Writing a focused data engineer job description sets the foundation for successful hiring. Remember to customize the templates above based on your specific tech stack and business needs. Clear job requirements will help candidates self-assess their fit, while detailed responsibilities will give them insight into daily tasks.
Once you publish the role, prepare to screen applications and conduct technical interviews to assess candidates’ skills. Focus on their ETL expertise, how they can work with a large amount of data, configure cloud data lakes, and work with your existing database solutions. You can also include some Python programming exercises and SQL tasks as a test. Also, don’t forget to assess their soft skills and cultural fit.
At DOIT, we can simplify this process and connect you with vetted data engineers who match your technical requirements and team culture. Our talent pool includes over 650 experienced data specialists from Europe, LATAM, and North America. Contact us to discuss your data engineering needs and receive the first relevant CVs within 5 business days.
Get a consultation and start building your dream team ASAP.
Request CVsA data engineer designs, builds, and maintains the systems that collect, store, and process data for organizations. They create data pipelines that move information from various sources into databases or data warehouses. Data engineers work with Python, SQL, and cloud platforms to build infrastructure that supports analytics and business intelligence efforts.
Data engineers need strong programming skills in Python and SQL, along with experience in cloud platforms like AWS, Azure, or GCP. They must understand database design, ETL processes, and data pipeline development. Additionally, knowledge of big data frameworks (e.g., Apache Spark) and workflow orchestration tools (e.g., Airflow) is a fundamental requirement for most data engineering roles.
Data engineers build and maintain data pipelines and write scripts to extract and transform data. They monitor system performance, troubleshoot data quality issues, document processes, and collaborate with data scientists and analysts to ensure reliable data delivery.
