Introduction

Data science is the interdisciplinary field of using scientific methods, algorithms, and systems to extract knowledge and insights from structured and unstructured data, combining mathematics, statistics, computer science, and domain expertise to collect, analyze, and visualize data, ultimately with the intention of informing decision-making and solving complex problems.

However, supply and demand poses a challenge for companies looking to hire data science professionals, with the demand for qualified, experienced data engineers and analysts outweighing the domestic supply within the US. Businesses often amplify this challenge by restricting their search to local tech talent; however, in recent years, the rise of remote, global work has begun to solve this issue.

What is Data Engineering?

Data engineering is the practice of designing, building, and maintaining the systems that collect, store, process, and transform raw data into a usable format, creating the essential foundation for data analysis, business intelligence, and machine learning. It encompasses data architecture, pipelines (ETL/ELT) transformation, storage and management.

What does a Data Engineer do?

Data engineers design, build and maintain the infrastructure that allows organizations to collect, store, and process large amounts of data by creating pipelines to move and transform raw data from various sources into a usable format. This involves working with big data technologies, databases, and programming languages like Python and SQL.

Data Engineering Skills and Qualifications

Data Engineers will often hold a degree in computer science or similar, though this is not essential. When hiring a Data Engineer, it’s most important to look for strong experience in key tools and frameworks.

There are also many certifications and qualifications that a data engineer may have. Popular certifications include:

• Microsoft Azure Data Engineer Associate
• Google Professional Data Engineer
• AWS Certified Big Data – Specialty
• Cloudera Data Engineer
• IBM Data Engineering Professional Certificate
• AWS Certified Data Engineer
• Databricks Certified Data Engineer Professional
• SAS Certified Big Data Professional
• Arcitura Certified Big Data Architect (BDSCP)
• DASCA Associate Big Data Engineer
• SnowPro Advanced Data Engineer

Beyond technical skills, soft skills are vital when choosing your Data Engineer. Problem-solving is crucial, aiding with troubleshooting and continuous learning. Communication is also essential when looking for any new team member, particularly in facilitating clear and open communication between technical and non-technical stakeholders.

Popular Languages for Data Engineering

There are many languages and frameworks commonly used for Data Engineering. Some of the most popular include:

• Python: An open-source, general-purpose programming language with broad applicability in the data science industry and across other domains.
• R: An open-source, domain-specific language, explicitly designed for data science and popular in finance and academic contexts.
• SQL: A domain-specific language that allows programmers to communicate with, edit and extract data from databases.
• Java: An open-source, object-oriented language, known for its first-class performance and efficiency.
• Julia: An increasingly popular language for data science due to its speed, clear syntax and versatility.
• Scala: A multi-paradigmatic language explicitly designed to be a clearer and less wordy alternative to Java.

Popular Data Engineering Frameworks

Frameworks are collections of reusable code, templates and tools that provide a foundational structure for building applications effectively and efficiently. Some of the most popular data engineering frameworks include:

• Apache Spark: In-memory processing engine for large-scale batch and stream data.
• Apache Hadoop: Framework for distributed storage (HDFS) and processing of big data.
• Apache Flink: Unified engine for stream and batch data processing.
• Apache Airflow: Defines, schedules, and monitors data pipelines (DAGs).
• Dagster: Modern data orchestrator focusing on data assets and pipelines.
• dbt (Data Build Tool): SQL-based tool for transforming data in the warehouse.
• Apache Kafka: Distributed streaming platform for high-throughput data feeds.
• Redpanda: Kafka-compatible streaming platform.
• Kubernetes: For containerization and orchestrating data services.
• Terraform: For managing infrastructure as code (IaC).

What are some popular tools in Data Engineering?

There are many tools commonly used in data engineering, including:

• Snowflake: Cloud-native data platform for analytics.
• Google BigQuery: Serverless, scalable data warehouse on GCP.
• Amazon Redshift: Cloud data warehouse on AWS.
• Databricks: Unified data analytics platform.
• Talend: Comprehensive data integration and ETL platform.
• Airbyte: Open-source data integration platform.
• Tableau: Powerful business intelligence and visualization.
• Metabase: Open-source self-service BI.

What are the benefits of Data Engineering?

Data engineering is vital to businesses because it allows them to build robust systems to collect, store, and process vast amounts of data, enabling data-driven decisions and improving business performance. It transforms raw data into accessible, reliable assets, supporting everything from AI/ML models to strategic planning. Other benefits include:

• Decision-Making: Provides users with accurate, timely data, allowing them to make data-driven decisions.
• Operational Efficiency: Streamlines data workflows and reduces manual overhead by automating processes.
• Scalability: Builds infrastructure to handle increasing data volumes.
• AI/ML: Creates the foundation for advanced analytics and machine learning models.

How much does it cost to hire a Data Engineer?

When hiring traditionally in the US for a local, mid-level data engineers, the average annual compensation is $150,372, with a range of $50,000 to $300,000 according to Built In. However, even by just expanding to other parts of the United States, salaries can be reduced significantly.

On average, companies hiring data engineers with HireDevelopers can expect to save between 50% and 80% on salaries and hiring costs, with exact salaries varying based on location and experience. The greatest savings can be found by hiring in Africa and Asia, while companies looking for aligned time zones and a closer cultural match can hire from our extensive pool of Latin American developers.

How to hire Data Engineers

The first step to hiring a data engineer is to decide on a hiring method. Traditional hiring methods include local job adverts, LinkedIn job posts and using your professional network to identify potential candidates.

However, these methods are often time consuming and can be expensive. The median time to hire a developer is around 41 days, with the slowest 10% taking up to 82 days and 38% of companies reporting project delays due to recruitment problems, according to the Linux Foundation’s 2024 State of Tech Talent Report.

Alternatively, many companies are now turning to services like HireDevelopers to cut costs and speed up the hiring process. With HireDevelopers, you can hire data engineer from anywhere in the world, at any price point, in just 24 hours.

When putting together a job description or job interview for a data engineer, prioritize technical skills, like familiarity with key frameworks and libraries.

You should also consider using coding challenges alongside a traditional interview during the hiring process – if you’re using a pre-vetted talent pool like HireDevelopers, this has already been done for you. Coding challenges allow you to assess problem-solving skills and coding expertise in a realistic, real-time environment.

If you need help putting together a data engineer job description or conducting a data engineer job interview, you can check out our helpful and comprehensive guides below. Alternatively, to cut your time-to-hire by 90% and unlock savings of up to 80%, get in touch and book your free HireDevelopers consultation today.

Why hire Data Engineers with HireDevelopers?

Hiring the right data engineer for your team is a huge undertaking, and the process can be intimidating, especially for startups and small businesses.

At HireDevelopers, our pre-vetted, global tech talent marketplace streamlines your hiring journey and cuts time-to-hire by over 90% without compromising on quality or stretching your budget. Receive a custom shortlist of top data engineers at any seniority level, at any price point and located anywhere in the world within just 24 hours of your consultation.

Plus, we’ll handle all payroll, legal compliance and HR completely for free, giving you more time to start building with your new data engineer.

Get in touch today and start hiring top-notch data engineers in just 24 hours.