Data Engineering Career Path: How to Advance Your Career in the Field

  • January 1, 2023
  • 5 mins to read
  • blog
Tags :> data data_engineering cloud
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As a data engineer, you play a crucial role in helping organizations derive insights and make data-driven decisions. From building data pipelines to managing large datasets, the responsibilities of a data engineer are vast and varied. To succeed in this field, it’s important to have a solid foundation in the skills and technologies outlined in our data engineering roadmap.

But mastering data engineering goes beyond just learning the tools of the trade. It also involves staying up-to-date with the latest technologies and best practices, as well as developing a strong understanding of how data flows through an organization.

Here is a detailed breakdown of each point on the roadmap, with code examples where appropriate:

  1. Learn SQL : As a data engineer, you will be working with large datasets and SQL will be your bread and butter for querying and manipulating data. Some basic SQL concepts you should familiarize yourself with include:
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SELECT statement: used to select data from a database
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SELECT * FROM Customers;
WHERE clause: used to filter the results of a SELECT statement
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SELECT * FROM Customers WHERE Country='Mexico';
JOINs: used to combine data from multiple tables
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SELECT Customers.CustomerName, Orders.OrderID
FROM Customers
INNER JOIN Orders ON Customers.CustomerID=Orders.CustomerID;
  1. Learn a programming language : Data engineering often involves building pipelines to process and move data, so it’s important to have at least one programming language under your belt. Some popular choices for data engineering include Python and Java. Here is a simple example of a Python script that reads in a CSV file and prints out the total number of rows:
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import csv

with open('data.csv') as csv_file:
    csv_reader = csv.reader(csv_file, delimiter=',')
    row_count = 0
    for row in csv_reader:
        row_count += 1

print(f'Total rows: {row_count}')
  1. Learn a big data processing framework : In order to work with large datasets, you will need to learn how to use a big data processing framework such as Apache Hadoop or Apache Spark. Here is an example of how to use Spark to count the number of lines in a text file:
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from pyspark import SparkContext

sc = SparkContext()
text_file = sc.textFile("hdfs:///path/to/file.txt")
counts = text_file.count()
  1. Learn a data storage technology : As a data engineer, you will need to know how to store and retrieve data efficiently. Some popular technologies for storing large datasets include Hadoop HDFS, Apache Cassandra, and Amazon S3. Here is an example of how to use the Amazon S3 API to list all the objects in a bucket:
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import boto3

s3 = boto3.client('s3')
objects = s3.list_objects(Bucket='my-bucket')['Contents']
for obj in objects:
    print(obj['Key'])
  1. Learn a data streaming technology : Data engineering often involves processing data in real-time, so it’s important to know how to work with data streams. Some popular technologies for data streaming include Apache Kafka and Amazon Kinesis. Here is an example of how to use the Kafka Python client to consume messages from a topic:
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from kafka import KafkaConsumer

consumer = KafkaConsumer('my-topic', bootstrap_servers=['kafka:9092'])
for message in consumer:
    print(message.value)
  1. Learn a data visualization tool : Data visualization is an important part of data engineering, as it helps to communicate the insights derived from data analysis. Some popular tools for data visualization include Tableau and Google Charts. Here is an example Using Google Charts, you can create a bar chart to visualize data as follows:
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<html>
  <head>
    <script type="text/javascript" src="https://www.gstatic.com/charts/loader.js"></script>
    <script type="text/javascript">
      google.charts.load('current', {'packages':['bar']});
      google.charts.setOnLoadCallback(drawChart);

      function drawChart() {
        var data = google.visualization.arrayToDataTable([
          ['Year', 'Sales', 'Expenses'],
          ['2013',  1000,      400],
          ['2014',  1170,      460],
          ['2015',  660,       1120],
          ['2016',  1030,      540]
        ]);

        var options = {
          chart: {
            title: 'Company Performance',
            subtitle: 'Sales, Expenses, and Profit: 2013-2016',
          }
        };

        var chart = new google.charts.Bar(document.getElementById('chart_div'));

        chart.draw(data, options);
      }
    </script>
  </head>
  <body>
    <div id="chart_div"></div>
  </body>
</html>
  1. Learn how to use cloud computing platforms : Many data engineering tasks are carried out on cloud computing platforms such as Amazon Web Services (AWS) and Google Cloud Platform (GCP). It’s a good idea to learn how to use one or both of these platforms. Here is an example of how to use the AWS SDK for Python (Boto3) to list all the instances in an AWS Elastic Compute Cloud (EC2) region:
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import boto3

ec2 = boto3.client('ec2')
response = ec2.describe_instances()
for instance in response['Reservations']:
    print(instance['Instances'][0]['InstanceId'])

Here are a few pieces of career guidance for those interested in pursuing a career in data engineering:

  • Focus on learning the skills listed in the roadmap: To become a successful data engineer, it’s important to have a strong foundation in SQL, a programming language, big data processing frameworks, data storage technologies, data streaming technologies, data visualization tools, and cloud computing platforms.

  • Gain practical experience: In addition to learning the necessary skills, it’s important to get hands-on experience working with data. This could involve completing online courses or projects, internships, or working on personal projects.

  • Network and build a portfolio: Networking with other professionals in the field and building a portfolio of projects you’ve worked on can be incredibly helpful in getting a job as a data engineer. Attend meetups and conferences, and consider joining online communities or forums to connect with others in the industry.

  • Consider obtaining a certification: Obtaining a certification, such as a Cloudera Certified Developer for Apache Hadoop (CCDH) or AWS Certified Big Data - Specialty, can demonstrate to potential employers that you have a strong foundation in data engineering and are serious about your career.

  • Stay up-to-date with the latest technologies: The field of data engineering is constantly evolving, so it’s important to stay up-to-date with the latest technologies and best practices. Consider subscribing to industry newsletters and blogs, and consider earning continuing education credits to ensure that your skills remain current.