Organizations have realized the value data can bring to their business and are looking forward to modernizing their analytics environments. This has led to increasing demand for data engineers who can unlock the value of data by removing the bottlenecks across the entire data team.
To tackle this growing backlog of data engineering tasks, the existing data engineers are leveraging tools such as automation, low- and no-code technology, leading to a new era of citizen data engineers. 78% of data professionals are given responsibilities outside their core functions since the onset of the COVID-19 pandemic. In brief, data science professionals are turning to automated solutions, increasing the team’s bandwidth, and making a sincere attempt to solve the existing team’s challenges.
By definition, data engineering is the practice of making the right data accessible and available to various data consumers like data analysts, data scientists, business analytics, and business users. It involves collaboration across business and IT (Gartner).
The role of data engineers today is different as compared to yesteryears. From being support for analytics purposes they are turning into the owner of data-flows. 25% of data engineers use low- and no-code solutions and 53% use technology, research study says.
The adoption of cloud, use of technologies such as Spark, Kafka, and serverless has enabled the faster processing of multi-latency petabyte-scale data through auto-scaling and auto-tuning. The data engineering role has evolved from a focus on loading and storing data to computing and extracting data and orchestrating with the use of right tools and technologies. Also, new tools allow them to focus on core data infrastructure, performance optimization, and custom data pipelines and its orchestration.
Data engineers work with data consumers like data analysts, data scientists, systems architects, and business leaders. Let’s explore the data engineer’s role in 2021 here.
Evolving responsibilities of data engineers
As we find a move away from using traditional ETL tools and use of new sophisticated tools and technologies for handling big data, the term ‘data engineering’ is evolving. Data engineering focuses on data — infrastructure, warehousing, mining, modeling, crunching, and management.
Data engineers perform different tasks like data acquisition, cleansing, conversion, disambiguation, and de-duplication. Despite the rise in automation, data engineers play a significant role in the team. The only change is that their priorities and tasks have got shifted. In this evolving big data industry, the main role of data engineers includes:
👉Data gathering: They determine the period of data storage, who and how will use them to give access to the data.
👉Metadata maintenance: They determine the schema, size, source, and securing of the data. They are the data owners in the true sense.
👉Data governance: They ensure data security through centralized controls like data encryption, data auditing, and LDAP.
👉Data storage: They use specialized and modernized technologies and tools optimized for data usage and storage.
👉Data processing: They process the data as per specific needs using tools, enrich data, summarize, and store them for appropriate usage.
In other words, with the availability of new tools and technologies, the data engineers are more focused on management and optimization of core data infrastructure, maintenance of custom ingestion pipelines, support for data team resources, and build non-SQL transformation pipelines.
- Data engineers at Airbnb have built Airflow.
- Data engineers at Netflix are responsible for maintaining a sophisticated infrastructure.
- Uber data engineers use metadata to tune infrastructure as required.
So, the organizations look for data engineers who are well-versed in basic software engineering methodologies (like Agile, DevOps, and service-oriented architecture), distributed systems, Open frameworks, SQL, Python, Visualization, Cloud platforms, Analytics, and data modeling.
Further, the ever-increasing cyber threats have compelled data engineers to learn about cloud security best practices, management of data privacy, and vigilance while handling data. They are expected to discover the right dataset through an intelligent data catalog, bring the right data with mass ingestion, operationalize data pipelines, process data at scale in real-time, desensitize confidential data through intelligent data masking, and ensure trusted data availability.
The demand for data engineers
Over the last few months, we can see a growing interest in using technical skills testing platforms for big data engineering roles.
Data engineering is one of the hottest tech jobs now. Data engineers today help data scientists and analysts find the right data, make it available, ensure data masking, spend less time on data preparation, and operationalize data pipelines. According to Datanami, there are 4x more jobs for engineers than for scientists.
LinkedIn’s 2020 Emerging Jobs Report says that data engineering is one of the top-ten jobs experiencing tremendous growth. Likewise, the World Economic Forum (WEF) 2020 Jobs of Tomorrow Report indicates data engineer as one of the top three emerging data and AI-related jobs for the coming years.
As companies accelerate their move toward digital transformation, we will continue to see that the role of data engineers grows. The average data engineer salary in the United States is USD 106,843. The range typically falls between USD89,405 and US124, 661 as per salary.com. according to indeed.com, the average salary is USD131,129 with USD 5000 cash bonus per year.
If this evolving role interests you, and you are excited to gain expertise in new tools and technologies, earning a big data engineering certification will be one of the best ways.
Sail through the roaring waves by equipping yourself with the current knowledge, skills, technologies, and tools.