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Tony Lopykinski

Founder & Managing Principal

8 Essential Skills Every Data Engineer Should Master

In our previous blog, Why Data Engineering is Essential for Data Science Success, we discussed the necessity for robust data engineering capabilities to pave the way for successful data science initiatives. We outlined key steps that data analytics leaders should consider when they want to optimize their data science efforts.

One critical aspect we highlighted is the essential skill sets to look for when hiring and nurturing data engineers. This post will delve deeper into those vital data engineering skills. Whether you are the leader responsible for building a robust data ecosystem, or a data engineer looking to advance your career, understanding the necessary skills of a data engineer is crucial to achieving your objectives:

1. Proficiency in programming languages

Data engineers must master programming languages that shape, manipulate, and analyze large data volumes. Becoming fluent in these languages is like being bilingual in the digital world, with each having its own nuances and best practices. Here are some of the most common data engineering programming languages:

Python

Known for its simplicity and readability, Python is now the go-to tool in the data engineering domain. Its rich ecosystem and libraries like Pandas and NumPy make it indispensable for data manipulation and pre-processing. A data engineer fluent in Python can offer more efficient and scalable solutions, from data ingestion to the construction of data pipelines. Python is essential for working with CICD pipelines or data pipelines in any cloud platform, making it a must-have skill for a data engineer.

SQL

Fundamental to database management systems, SQL proficiency is non-negotiable for any data engineer. Whether it’s crafting complex queries, optimizing databases, or understanding the intricate relationships within the data, SQL is the universal language in the world of data manipulation and retrieval. SQL and Python together make a formidable combination. A data engineer versed in these two programming languages may not need anything more to get the job accomplished in most scenarios. 

YAML

Yaml is an essential skill for putting together modern CICD/data pipelines. YAML (YAML Ain’t Markup Language) is a human-readable data serialization language commonly used in data engineering for its simplicity and flexibility. It provides a concise yet expressive way to represent data structures using indentation and key-value pairs. In data engineering, YAML finds extensive application in configuration files, data pipelines, and workflow orchestration. Its lightweight syntax allows engineers to define complex data schemas, configurations for ETL (Extract, Transform, Load) processes, and deployment specifications with ease.

2. Data modeling and database design expertise

Like an architect, data engineers craft blueprints that guide the construction of robust data systems. Understanding data modeling principles and possessing the ability to translate business requirements into a clear database design are crucial skills for any data engineer, including:

Relational databases

Grounded in the solid principles of normalization and ACID (Atomicity, Consistency, Isolation, Durability), data engineers often start their design journey with relational databases. Expertise in database systems like MySQL, PostgreSQL, and SQL Server is imperative for constructing and maintaining relational data models.

NoSQL databases

With the rise of unstructured and semi-structured data, NoSQL databases such as MongoDB, Cassandra, and Redis provide flexible storage that aligns well with the evolving data paradigms. A data engineer must recognize when a NoSQL approach is more prudent than a traditional relational model and apply it accordingly.

Data warehousing principles

Data warehouses are the repositories of historical data, and understanding the nuances of designing for these systems is pivotal. Skills in schema design, indexing strategies, and query optimization tailored for these read-optimized databases empower data engineers to build efficient analytical platforms that support business intelligence initiatives.

Columnar MPP databases, including cloud

More recently, analytical databases have shifted towards cloud-     based columnar databases like Snowflake, AWS Redshift, Azure Synapse, etc. This architecture requires a different kind of structure, which is a departure from traditional dimensional modeling architecture and prioritizes a flat representation of the data (ELT rather than ETL), making insertions into the data warehouse quicker and simpler.  As a result, “data vault architecture” is becoming more commonplace to keep the similar usage and functionality of      a traditional data warehouse. Hence, a data engineer must be proficient in both approaches. 

3. Experience with big data technologies

Growing volumes of data have ushered in technologies that specialize in handling massive datasets. Familiarity with these big data tools is no longer a luxury but a necessity for data engineers navigating through today’s data-intensive environments.

Hadoop

The cornerstone of the big data revolution, Hadoop, with its distributed file system (HDFS) and MapReduce processing paradigm, was the early go-to solution for parallel processing large datasets. While Hadoop is losing its popularity, it is still implemented at some organizations. Recently, Amazon S3, Azure ADLS, and have been taking over the HDFS functionality of Hadoop, and technologies like EMR (AWS) are replacing MapReduce. We will see this trend continue. 

Apache Spark

The next iteration in big data processing, Apache Spark, offers a more expressive and powerful engine that takes advantage of in-memory computing. Its ability to run complex data transformations in parallel across a cluster, with a clear and concise API, has made it a staple in the data engineer’s toolkit. Most Apache Spark implementations are in complete collaboration with cloud platforms like Azure and Databricks. 

Apache Kafka

In the domain of real-time data streaming, Apache Kafka’s publish-subscribe messaging system creates opportunities for data engineers to process and analyze data as it flows, enabling real-time decision-making by businesses. Kafka is becoming very popular for streaming data, and cloud platforms have adopted it natively. For example, Amazon Data Firehose is strongly integrated into the AWS platform.

 

4. Proficiency in data warehouse and data lake concepts

Understanding the fundamental concepts of data warehouses and data lakes is vital for a data engineer to thoroughly understand how data is stored, organized, and served in an enterprise setting. Key concepts are:

Data warehousing

Conceptualizing data warehouse tables in dimensional and normalized schemas provides the foundation for optimized query performance and structured data analysis. Data engineers must understand the design and implementation of these schemas to facilitate efficient business intelligence reporting. In addition, Snowflake and other MPP systems provide an alternative modeling approach for data warehousing where dimensions and facts are combined to create and maintain fewer tables.

Data lakes

Data lakes provide the ability to store data in its raw, unaggregated form. Skills in designing and managing these immense repositories require a vision that balances storage efficiency with the flexibility to answer unforeseen questions. Data lakes have become an essential component of data strategy, serving as the lowest layer of data acquisition. These lakes are usually based on cloud data storage methodologies including S3, ADLS, or Google Cloud Storage. 

Data virtualization

A more recent addition, data virtualization technology encapsulates the idea of a decoupled layer above data sources, enabling data engineers to present a unified view without the need for massive ETL operations. External tables have evolved recently, enabling SQL querying of data in the acquisition layer within storage services like S3, ADLS, or Google Cloud Storage using tools such as AWS Glue or Databricks Delta . This trend is expected to grow in the future, aligning with the concept of ELT versus ETL.

5. Familiarity with data integration and ETL tools

Proficiency in tools that facilitate the movement and transformation of data is a practical skill that data engineers must hone. These tools simplify and automate complex data processes, increasing operational efficiency and reducing errors.

Traditional ETL tools

Traditional tools like Informatica, DataStage, and Talend have long been stalwarts in the data integration space, offering a suite of tools for ETL operations. These tools remain a must-know for data engineers working with legacy systems. More recently, orchestration tools like Apache Airflow or DBT with Snowflake are becoming more popular. 

Apache NiFi

NiFi, with its powerful, user-friendly interface and robust data flow capabilities, has quickly gained ground. An understanding of NiFi’s role in a modern data architecture is invaluable for today’s data engineers     .

Cloud-based ETL

With the proliferation of cloud services, ETL tools have seen a shift to the cloud, offering scalable solutions. Data engineers must be adept at navigating platforms like AWS Glue, Google Dataflow, or Azure Data Factory. Another aspect of cloud-based ETL is serverless computing using constructs like AWS Lambda. These components are becoming essential for putting together ETL pipelines.

ELT vs ETL

There is also an increasing trend towards ELT vs traditional ETL. The aim is to load the data into data appliances like Snowflake and then do the transformation using SQL embedded in tools like DBT, leveraging the MPP columnar architecture for computation rather than an external tool and server. 

6. Expertise in cloud-based data platforms

The cloud has revolutionized the data engineering landscape, providing elastic scalability and cost-efficiency. Proficiency in one or more cloud-based platforms is now an industry standard.

Amazon Web Services (AWS)

Data engineers well-versed in AWS can set up scalable, flexible, and cost-effective data storage and processing solutions using services like S3, Redshift, and Glue.

Microsoft Azure

Microsoft’s cloud platform offers a rich set of data services, from Azure Data Factory to Azure Synapse Analytics, empowering data engineers with the building blocks to create and manage enterprise-grade data solutions.

Google Cloud Platform (GCP)

With solutions like BigQuery, Cloud Dataflow, and Cloud Dataprep by Trifacta, GCP provides efficient tools to manage and analyze large datasets. Data engineers proficient with GCP can tap into advanced machine learning capabilities to enhance data-driven decision-making processes.

7. Business domain knowledge

Data engineers are more than just technologists—they are bridge builders who must comprehend the business context and domain in which they operate. Without this understanding, the data solutions they provide may lack relevance or fail to address actual business needs.

Vertical industry experience

Data engineers must familiarize themselves with the specific challenges and data models of the vertical industries they serve, be it healthcare, finance, retail, or manufacturing. Each industry has unique data requirements and compliance regulations that must be navigated and understood.

Functional solutions

A comprehensive grasp of functional domain solutions for common data problems, such as customer relationship management (CRM), enterprise resource planning (ERP), and content management, will greatly enhance a data engineer’s ability to provide tailored data solutions.

Data governance principles

Understanding data governance principles and collaborating with data stewards in the business are indispensable skills for a data engineer to ensure data quality, security, and compliance with enterprise policies.

8. Strong interpersonal and problem-solving skills

Collaborative skills and the ability to communicate effectively are as crucial as technical acumen. Problem-solving is a daily event, and data engineers must be adept at tackling challenges from multiple angles.

Team collaboration

Data engineers work alongside data scientists, analysts, and other stakeholders to meet organizational goals. They must be effective team members, capable of conveying complex concepts in clear terms and understanding and incorporating their colleagues’ insights.

Flexibility and adaptability

The data engineering landscape is constantly evolving, with new tools, technologies, and methodologies emerging. The ability to adapt quickly, learn on the go, and apply new knowledge is an essential attribute.

Analytical thinking

Data engineers must approach problems with a logical, analytical mindset, breaking complex issues down into manageable parts to reach an optimal solution. An affinity for data and patterns is an invaluable asset in this profession.

Essential data engineering skills

Critical data engineering skills enable an organization’s success

Data engineering is the foundation of successful data science initiatives, thus having competent data engineers with the right skillset is crucial for organizations to unlock the full potential of their data. From programming languages and database design to cloud-based platforms and business domain knowledge, these essential skills will allow data engineers to effectively collect, transform, and analyze vast amounts of data to drive informed decision making. So, whether you are a seasoned data engineer looking to upgrade your skills or an organization seeking top-notch talent in this field, be sure to prioritize the essential skills outlined above.

As leaders in the field of data analytics consulting, Mavent Analytics understands the importance of skilled data engineers. Whether it’s through data management consulting or talent planning and acquisition services, we are committed to helping organizations cultivate these talents within their teams. As always, we are here to support you at any stage of your journey toward harnessing the power of your data.

 

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