How to Hire a Data Engineer: Upstaff’s Step-by-Step Guide
Let’s consider a TOP Data Engineer Profile:
Data engineers need a blend of technical and soft skills. Key technical skills include programming (Python, Java, Scala), database management (SQL, NoSQL), data warehousing, big data technologies (Hadoop, Spark), and cloud computing platforms (AWS, Azure, Google Cloud). Soft skills like problem-solving, communication, and critical thinking are also essential for success.
- Programming Languages:
Python, Java, and Scala are commonly used for data manipulation, building data pipelines, and working with big data tools.
- Database Management:
A strong understanding of both relational databases (like MySQL, PostgreSQL) and NoSQL databases (like MongoDB, Cassandra) is crucial.
- Data Warehousing:
Knowledge of data warehousing concepts and technologies (e.g., Snowflake, Redshift) is essential for building and managing large-scale data storage and analysis systems.
- Big Data Technologies:
Experience with Hadoop, Spark, Hive, and Kafka is often required for handling large volumes of data.
- Cloud Computing:
Proficiency in cloud platforms like AWS, Azure, or Google Cloud is increasingly important for deploying and managing data infrastructure.
- Data Modeling:
Understanding different data modeling techniques (e.g., star schema, snowflake schema) is important for designing efficient data storage and retrieval systems.
- ETL Tools:
Familiarity with ETL (Extract, Transform, Load) tools like Apache Nifi, Talend, or Apache Airflow is necessary for building data pipelines.
- Data Architecture:Designing and implementing robust and scalable data architectures that meet business needs.
What is a data engineer?
A data engineer is someone who processes data before it’s analysed or used for work. Most roles involve designing and creating data collection, storage and analysis systems.
Data engineers will usually focus on creating data pipelines to aggregate data from records. They are software engineers who collect and amalgamate data, meld the desire for data accessibility and optimisation of their organisation’s big data portfolio.
The amount of data an engineer needs to manage also reflects on the organisation he works for, and more specifically the size of the organization. The bigger the enterprise, the more advanced the analytics will typically be, and thus the amount of data the engineer will need to manage will rise in tandem. There are data-intensive industries, such as healthcare, retail, and finance.
Data engineers work with dedicated data science teams to bring information into the light, so that businesses can make better business decisions. They draw upon their experience to link all of the individual records until the lifecycle of the database is complete.
The Data Engineer Role
The process of sanitising and cleaning up data sets falls to the socalled data engineers, who serve one of three broad functions:
- Generalists.
Generalist data engineers work on small teams and are able to capture, consume and transform data end-to-end, and will have more expertise than most data engineers (less system architecture). Any data scientist transitioning into data engineering would fit well into the generalist focus.
For instance, a generalist data engineer might be engaged in a project to build a dashboard for a small local food delivery company showing how many per day deliveries they made over the past month and how many deliveries they are expected to make next month. - Pipeline-focused data engineer.
The data engineer of this variety typically belongs to a data analytics team and more advanced data science projects are distributed over distributed systems. A position like this is more likely to be found at medium- to large-sized enterprises.
A local, regional food deliveries company might want to do a pipeline-like approach and create an analyst tool where data scientists search through metadata to extract delivery information. She might calculate how many miles they’ve driven and how long they’ve driven to deliver goods during the last month, and feed that data into a predictive algorithm that predicts how those numbers should shape their business in the future. - Database centric engineers.
The data engineer hired by a large corporation deploys, maintains and populates analytics databases. Only when there are multiple databases does this role exist. So, these engineers implement pipelines, might calibrate databases for specific analyses, and devise table schema through extract, transform and load (ETL) to import data from multiple sources into a single system.
For a database-based application at a large, national food delivery company, this would mean building an analytics database. Aside from creating the database, the developer would also develop code to load that data from where it’s collected (the primary application database) into the analytics database.
Data Engineer responsibilities
Often, data engineers are part of an existing analytics team, working alongside data scientists. Data engineers deliver data in a digestible format to the scientists who execute queries on the datasets or algorithms to run predictive analytics, machine learning and data mining types of processes. Data engineers also deliver aggregated information to business managers, analysts, and other business end-users to extract and use such insights for better business operations.
Data engineers work both on structured and unstructured data. Structured data is information organized in a structured storage unit, such as a structured database. Data that’s unstructured, like text, pictures, audio, and video files, doesn’t exactly conform to standard data models. To work with both types of data, data engineers need to be familiar with classes of data architecture and applications. In addition to the basic data types manipulation skills, the data engineer’s sledgehammer should contain several big data technologies as well: the data analysis pipeline, the cluster, the open source data ingestion and processing stack, etc.
Actual responsibilities may vary from organization to organisation, but here are some common job descriptions for data engineers:
- Create, run and maintain database pipelines.
- Create methods for data validation.
- Acquire data.
- Clean data.
- Develop data set processes.
- Improve data reliability and quality.
- Create algorithms to interpret data.
- Preparing data for predictive and predictive modelling.
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