What Does a Data Engineer Do?
Data engineers design, build, and maintain the infrastructure that stores, processes, and analyzes large amounts of data. Data engineers are responsible for the overall architectures for data pipelines that enable organizations to make data-driven decisions by collecting and utilizing data.
As part of their role as data engineers, professionals will work closely with data scientists, business analysts, and other stakeholders to understand their data needs and design systems that meet those needs. In addition, they ensure that data is easily accessible and usable by other organization members and that privacy, security, and governance standards are met.
Are you a job seeker?
Browse zengig’s
comprehensive list
of job openings
and apply online
National Average Salary
Data engineer salaries vary by experience, industry, organization size, and geography. To explore salary ranges by local market, please visit our sister site zengig.com.
The average U.S. salary for a Data Engineer is:
$113,900
Data Engineer Job Descriptions
When it comes to recruiting a data engineer, having the right job description can make a big difference. Here are some real world job descriptions you can use as templates for your next opening.
Example 1
Do you love building and pioneering in the technology space? Do you enjoy solving complex business problems in a fast-paced, collaborative, inclusive, and iterative delivery environment? At [Your Company Name], you’ll be part of a big group of makers, breakers, doers and disruptors, who solve real problems and meet real customer needs. We are seeking a data engineer who is passionate about marrying data with emerging technologies. As an ideal candidate, you have proven experience building data pipelines, transforming raw data into useful data systems, and optimizing data delivery architecture.
Typical duties and responsibilities
- Create, maintain, and test architectures
- Build large, complex data sets to meet functional/non-functional business requirements
- Identify, design, and implement internal processes to improve efficiency and quality
- Automate manual processes by using data
- Optimize data delivery
- Build analytic tools that provide actionable insights into performance metrics
- Work with executive, product, data, and design stakeholders to resolve data-related technical issues and support their data infrastructure needs
- Work with data and analytics experts to improve data system functionality
- Use programming language and tools
- Prepare data for predictive and prescriptive modeling
Education and experience
- Bachelor’s degree in computer science, information technology, or applied math
- Master’s degree a plus
- 5+ years of related experience
Required skills and qualifications
- Advanced knowledge of database systems like SQL and NoSQL
- Experience building and optimizing data pipelines, architectures, and data sets
- Experience performing root cause analysis on internal and external data and processes
- Exceptional analytical skills
- Experience manipulating, processing, and extracting value from large disconnected datasets
- Understanding distributed systems
- Knowledge of algorithms and data structures
- Good project management and organizational skills
Preferred qualifications
- Experience working in a fast-paced care facility
- Experience with data pipeline and workflow management tools
- Experience with AWS cloud services
- Experience with stream-processing systems
- Experience with Python, Java, C++, Scala, etc.
- Good communication collaboration, and presentation skills
Example 2
As a Data Engineer, you will be collaborating to build a robust and highly performant data platform using cutting-edge technologies. You will develop distributed services that process data in batch and real-time with a focus on scalability, data quality, and business requirements.
Must have skills
- Identify and implement improvements to our data ecosystem based on industry best practices
- Build, refactor and maintain data pipelines that ingest data from multiple sources
- Assembling large, complex sets of data that meet non-functional and functional business requirements
- Build ETL Pipelines. Build and support the tools we use for monitoring data hygiene and the health of our pipelines
- Automate processes to reduce manual data entry
- Ability to work with semi-structured and unstructured data
- Interact with data via APIs. Knowledgeable on the creation of API endpoints
Requirements
- Bachelor’s degree in Computer Science, Software Engineering, or related field required or equivalent combination of industry related professional experience and education
- Minimum 3 years in SQL and Python
- Azure or Amazon storage solution
- Experience building ETL Pipelines using code or ETL platforms
- Experience with Jira and Confluence
- Working knowledge on Relational Database Systems and concepts
Example 3
We’re looking for a strong, technically sound Data Engineer who is interested in working within a startup-oriented environment while having the backing of a large company. If that’s you, please read on.
You will
- Work with cross functional partners – Data Scientists, Engineers, and Product Managers to understand and deliver data needs
- Champion code quality, reusability, scalability, security, and help make strategic architecture decisions with the lead engineer
- Design, build, and launch extremely efficient and reliable data pipelines to move data across a number of platforms including Data Warehouse, online caches, and real-time systems
- Build product-focused datasets and scalable, fault-tolerant pipelines
- Build data quality checks, data anomaly detection, and optimize pipelines for ideal compute storage
Required experience and skills
- 3+ years of experience as a Data Engineer writing code to extract, ingest, process, and store data within SQL, NoSQL, and MPP databases like Snowflake
- Strong development experience with Python (or Scala/Java)
- Experience with complex SQL and building batch and streaming pipelines with Apache Spark framework
- Knowledge of schema design and dimensional modeling
- Experience with data quality checks, data validation and data anomaly detection
- Experience with workflow management engines like Airflow
- Experience with Git, CI/CD pipelines, Docker, and Kubernetes
- Experience with architecting solutions on AWS or similar public clouds
- Experience with offline and online feature engineering solutions for Machine Learning is a plus
Example 4
As a data engineer, you will extend and maintain the data pipelines that feed our ever growing data lake. Join a small autonomous team responsible for this data lake and its ingress and egress pipelines. Through this data lake and its data pipelines you will be providing immensely important data to internal business analysts, data scientists, leadership, as well as content partners in a multi-billion dollar industry.
Who is the role reporting to? Engineering Manager
Requirements
- BS/MS in computer science or equivalent experience in data engineering
- You love different types of data. i.e. content metadata, viewership metrics, etc.
- You love to solve difficult and interesting problems using data from various systems
- You have experience developing and maintaining software in Python
- You have experience with data pipelines that process large data sets via streams and/or batches
- You have experience in building services, capable of handling large amounts of data
- You have experience building and maintaining tests (unit, integration, etc.) that provide necessary quality checks. TDD experience is a plus
- You have experience with modern persistence stores, primarily SQL; however NoSQL experience is a plus
- You embrace best practices via pair programming, constructive code reviews, and thorough testing
- You thrive in an environment with rapid iterations on platform features
- You’re a team player and work well in a highly collaborative environment, which includes staff in remote locations
Details
- As a member of our team, you will:
- Be responsible for designing, building, and supporting components that compose the data lake and its pipelines
- Help build and extend our data lake by designing and implementing: data pipeline libraries and systems, internal analytics tooling / dashboards, and monitoring and alerting dashboards
- Provide support for the data pipelines including after-hours support on a rotational basis
- Work in a collaborative environment with other data engineers, data scientists, and software engineers to achieve important goals for the company
Candidate Certifications to Look For
- IBM Data Engineering Professional Certificate. The certificate is for entry-level candidates looking to stand out from their peers and develop job-ready data engineering skills. The self-paced online courses give candidates the essential skills they need to work with a variety of tools and databases to design, deploy, and manage structured and unstructured data. The course uses Python programming language and Linux/UNIX shell scripts where they’ll extract, transform and load (ETL) data. Candidates will gain a working knowledge of relational databases (RDBMS) and query data using SQL statements, among other things. With numerous labs & projects, they’ll get hands-on experience utilizing the concepts and skills they learn. There are no eligibility requirements for this credential.
- Cloudera Certified Data Engineer (CCP). If candidates are experienced open-source developers, earning the Cloudera Certified Data Engineer credential will demonstrate their ability to perform the core competencies required to absorb, transform, store, and analyze data in Cloudera’s CDH environment. Candidates interested in the CCP Data Engineer credential should have in-depth experience developing data engineering solutions. The program includes transferring data, storing data, data analysis, and workflow.
- Google Cloud Certified Professional Data Engineer. The Google Cloud Certified Professional Data engineer credential ensures that candidates can design, build, secure, and monitor data processing systems, emphasizing compliance, scalability, efficiency, reliability, and portability. The exam assesses their skills in designing data processing systems, using machine learning models, ensuring solution quality, and using data processing systems. There are no prerequisites or requirements for this credential, however, it is recommended that candidates have 3+ years of industry experience, including 1+ years designing and managing solutions using Google Cloud.
Sample Interview Questions
- Which ETL Tools are you familiar with?
- What skills are important for a data engineer?
- What data engineering platforms and software are you familiar with?
- Which computer languages do you have experience using?
- How do you create reliable data pipelines?
- What is the difference between structured and unstructured data?
- How would you deploy a big data solution?
- Have you engineered a distributed system? How did you engineer it?
- Have you used data modeling?
- Which frameworks and applications are essential for a data engineer?
- Are you more database or pipeline-centric?
- How would you validate a data migration from one database to another?
- What are the pros and cons of cloud computing?
- How would you prepare to develop a new product?
- Which Python libraries would you use for efficient data processing?
- How would you deal with duplicate data points in an SQL query?
- How would you plan to add more capacity to the data processing architecture to accommodate an expected increase in data volume?
- What is the difference between relational vs. non-relational databases?
- Can you explain the components of a Hadoop application?
