A data engineer's primary job responsibilities involve gathering, cleaning, linking and preparing data for analytical or operational uses. A data engineer is proficient in programmatically handling large amounts of data on sql, nosql and big data platforms. Data engineers typically work as part of analytics or IT teams in an organization. They sometimes also work directly with business users to deliver data aggregations or ad hoc reports. What differentiates a data engineer from a typical IT engineer is that data engineers go well beyond building data pipelines or performing ETL operations. Data engineers are proficient in developing and implementing algorithms such as discovery, sorting, aggregating, matching and data wrangling. They work on both structured and unstructured data sets and as a result they need to be experts in a variety of data extraction frameworks.
Data engineers are expected to have skills in a variety of programming languages such as Java, Python, Scala and SQL. They also need to have expertise in ETL tools as well as API development and deployment tools for creating and managing data pipelines for machine learning models, and providing data users(data scientists, business analysts) with simplified access to analysis ready data sets.
Hadoop based data lakes along with spark framework is the main play area for the data engineers. For that reason they are expected to be highly proficient in python and scala programming languages. They should be aware of all the open source libraries such as Panda or Scikit-Learn to perform their data tasks.
Hiring good data engineers has become a big challenge for the companies. Finding the right candidate with the right experience and right aptitude is a daunting task. Many companies struggle to justify the high salaries data engineers expect. Therefore it's very important that companies screen the right candidates right from the beginning and only bring the best which fit their specific needs and requirements.