---
# Shifting from a career in software development to data engineering? Here are some tips
**URL:** https://www.sigmoid.com/blogs/tips-for-shifting-career-in-software-development-to-data-engineering/
Date: 2021-09-14
Author: Sigmoid
Post Type: post
Summary: The role of a data engineer has gained prominence and sparked curiosity about the data engineer career path and the possibilities it...Read More...
Categories: Data Management
Tags: AI/ML, Cloud Transformation
Featured Image: https://www.sigmoid.com/wp-content/uploads/2021/09/Shifting-From-a-Career-in-Software-Development-to-Data-Engineering-Here-Are-Some-Tips-opt.jpg
---
The role of a data engineer has gained prominence and sparked curiosity about the data engineer career path and the possibilities it holds. With the rapid expansion of technology and the emergence of data as the lifeblood of modern enterprises, the question on many minds is,Is data engineering a good career choice?
According to research, data engineering interviews increased by 40% in the past year. This makes sense in a world that is navigating through a pandemic by rapid digital transformation. As more data is produced and consumed, businesses increasingly require experts who can manage large amounts of customer information, product performance results, and competitor research. Naturally, a data engineering career has become one of the most promising career options right now.
## Software Development vs. Data Engineering
In the current technological landscape, most of modern computing has to do with [cloud computing](/blogs/data-science-and-cloud-future-of-analytics/) — where businesses leverage the cloud, rather than storing all their data on local servers. Cloud computing has simplified and streamlined how most organizations perform business processes, offering faster collaboration and real-time data management. Almost everyone — software developers and data engineers alike — utilize many of the tools stored in the cloud, and this is partially why the two fields overlap.
In fact, both fields may even sound interchangeable to the everyday person. After all, both rely heavily on programming and have similar [skill sets](/blogs/10-must-have-skills-for-data-engineering-jobs/). However, the way they use these skills differs greatly and influences data engineer vs software engineer career growth. Software developers code to build and manage the increasingly complex products we use to navigate the cloud, and these skills are taught at all levels of higher education from bachelor's to master's degrees. Professionals with [software development master’s credentials](https://online.maryville.edu/online-masters-degrees/software-development/) focus on the creation, architecture, and presentation of software and applications. They build applications, create virtual reality platforms but only engage with data infrastructure in a limited capacity.
Software developers are mostly concerned with broad strokes and a “macro” approach to data. They are responsible for building infrastructures like large-scale applications, platforms, and systems. They also implement program codes to make these scalable systems function more efficiently. They’re less concerned with [cloud data warehouses](/ebooks-whitepapers/guide-to-cloud-data-warehouse/) or querying data, preferring to keep things upfront for a much simpler process.
On the other hand, data engineers maintain large datasets by building structures that house them. A data engineer stores, consolidates, and retrieves data so that accurate information is available to end-users — like software developers building systems and apps — which enable them to make crucial decisions. By building data infrastructures, data engineers play a critical role in enabling digital transformation associated with [artificial intelligence](/blogs/mlops-for-effective-ai-strategy/) (AI) and machine learning (ML) initiatives.
(Adapted from www.ryanswanstrom.com)
Data engineering is a specific skill that emerged from software engineering. However, a regular software engineer cannot be asked to perform the role of a data engineer because there are distinct activities and processes central to data engineering. For example, data engineers focus on the “micro” aspects of [data science and analytics](/blogs/data-science-and-cloud-future-of-analytics/). Data engineers would also be more familiar with handling [Big Data](/blogs/etl-on-cloud-transforming-big-data-analytics/), which are often complex data sets. Data engineers would have to build massive data reservoirs such as [data lakes](/ebooks-whitepapers/modern-data-architecture-data-lake/) that have highly scalable and [fault-tolerant](/blogs/fault-tolerant-streaming-workflows/) distributed systems.
## Tips on Shifting to Data Engineering
Shifting from software development to a career in data engineering is not uncommon, especially since developers already have a background in programming languages. However, to excel as a data engineer, you need to develop the ability to collect, query, and store information from databases. Here are three tips to help you shift to data engineering:
Improve your communication skills: Data engineers have to collaborate with colleagues who may not have technical expertise, so being able to communicate your thoughts clearly and without relying heavily on jargon is a must. Having strong communication skills ensures that you design and implement systems that are accessible to others.
Develop an “eye” for finding patterns: If you don’t love, or (at the very least) have an interest in data, you’re going to have a difficult time. A data engineer needs to enjoy finding patterns, identify new ways to create complex systems that work, and keep the big picture in mind.
Keep up to date with trends: As [data science](/blogs/data-science-and-cloud-future-of-analytics/) is a dynamic field, data engineers must constantly upskill so they can work well with data scientists, analysts, and architects. Trends in [concepts and technologies related to AI/ML ](https://www.zdnet.com/article/there-are-hundreds-of-thousands-of-jobs-in-ai-those-are-the-skills-you-need-to-get-one/) for automating processes, tools that can keep data management affordable, and data privacy compliance are just some of the things data engineers should be familiar with. You may also need to unlearn old skills; some coding languages like Python are still important, but it’s more critical to develop your skills in machine learning, algorithm design, and domain knowledge. With technology changing faster than ever, data engineers must be quick in adopting new tools.
For [data engineering services](/data-engineering/) that help you build and streamline your data processing pipeline for more value, talk to our team at Sigmoid today.
[lc_the_tags]
## Featured blogs
[lc_get_posts post_type="post"
posts_per_page="4" orderby="date" output_view="lc_get_posts_mycustom_view" output_number_of_columns="4"
output_wrapper_class="row" output_article_class="shadow" output_hide_elements="Excerpt"
output_excerpt_length="0" output_excerpt_text="Read More" output_heading_tag="span"
output_featured_image_format="thumbnail" output_featured_image_class="card-img-left" ]
## Share
[addtoany]
Subscribe to get latest insights
## Talk to our experts
Get the best ROI with Sigmoid’s services in data engineering and AI
## Suggested readings
[View all](/blogs/)
#### [10 Must-have Skills for Data Engineering Jobs](/blogs/10-must-have-skills-for-data-engineering-jobs/)
[Read blog](/blogs/10-must-have-skills-for-data-engineering-jobs/)
#### [5 Tips for Preparing Resume for a Data Engineering Interview](/blogs/5-tips-for-preparing-resume-for-a-data-engineering-interview/)
[Read blog](/blogs/5-tips-for-preparing-resume-for-a-data-engineering-interview/)
#### [10 Hacks to Prepare for Data Engineering Interviews](/blogs/10-hacks-to-prepare-for-data-engineering-interviews/)
[Read blog](/blogs/10-hacks-to-prepare-for-data-engineering-interviews/)
---
## Categories
- Data Management
---
## Navigation
- [Company](/about-sigmoid)
- [Newsroom](/newsroom)
- [Life at Sigmoid](/careers)
- [Takshashila](/takshashila)
- [Contact Us](/contact-us)
- [AI Strategy Blueprint your AI advantage](/enterprise-ai-strategy/)
- [Generative AI Drive innovation with Generative AI](/generative-ai/)
- [Responsible AI Build trust with ethical AI practices](/responsible-ai-in-enterprise/)
- [Agentic AI Reshape business with scalable agentic systems](/agentic-ai-solutions/)
- [AI Managed Services Ensure reliable AI performance](/ai-managed-services/)
- [Advanced Analytics Transform your business with data-driven insights](/advanced-data-analytics-solutions/)
- [Start Assessment](/agentic-ai-readiness-index/)
- [Data Strategy Strong data foundations for scalable AI](/data-analytics-strategy/)
- [Data Management Leverage data as a strategic asset](/ai-data-management-services/)
- [Data Ops Automate data for speed and quality](/data-devops/)
- [Data Engineering Deliver insights faster with scalable pipelines](/data-engineering/)
- [Cloud Transformation Modernize data to maximise efficiency](/cloud-migration/)
- [Download Whitepaper](/ebooks-whitepapers/building-data-products-in-a-data-mesh-to-drive-business-value/)
- [Data Modeling Structure data for better decisions](/data-modeling-services/)
- [Data Visualization Transform data into actionable stories](/data-visualization-service/)
- [BI Migration Enhance decision making with modern BI tools](/bi-migration/)
- [Data Observability Build trust with healthy, accurate data](/data-observability/)
- [Automated Insights Make smarter decisions with auto-generated insights](/automated-insights/)
- [Download Whitepaper](/ebooks-whitepapers/power-bi-hacks/)
- [CPG & Retail End-to-end analytics for planning, operations, and commercial excellence](/industries/cpg-analytics/)
- [Life Sciences Trusted intelligence across clinical, commercial, and operational workflows](/industries/life-sciences/)
- [Financial Services AI-powered analytics for risk, compliance and customer experience](/industries/banking-financial-analytics-services/)
- [Read case study](/case-studies/data-clean-room-enables-real-time-insights-to-improve-operational-efficiency/)
- [MediaIQ Advanced platform for in-flight marketing measurement](/accelerators/sigmoid-mediaiq-multi-touch-attribution-tool/)
- [CampaignIQ AI-driven platform for optimized campaign budget allocation](/accelerators/sigmoid-campaigniq/)
- [AssistBot GenAI email assistant that automates human-like responses](/accelerators/sigmoid-assistbot-for-ai-email-assistant/)
- [CreativeBot GenAI tool for personalized and brand-aligned creative design](/accelerators/sigmoid-creativebot/)
- [SocialBot GenAI platform to analyze digital conversations and trends](/accelerators/#marketing|socialbot)
- [DemandIQ Predict trends accurately and optimize inventory management](/accelerators/sigmoid-demandiq/)
- [NetworkIQ Track and optimize logistics operations in real-time to quickly address disruptions](/accelerators/sigmoid-networkiq/)
- [SupplyIQ End-to-end platform to optimize supply chain operations](/accelerators/sigmoid-supplyiq/)
- [ProcurementIQ Automated procurement operations for maximum savings, compliance and efficiency](/accelerators/sigmoid-procurementiq/)
- [RapidML Accelerated deployment for machine learning models](/accelerators/sigmoid-rapidml/)
- [DataGuard Comprehensive platform for proactive data quality management](/accelerators/data-quality-tool-sigmoid-dataguard/)
- [CloudPulse Cloud cost optimization platform with multi-cloud management](/accelerators/sigmoid-cloudpulse/)
- [RAPID GenAI foundation with built-in governance and cost clarity](/accelerators/sigmoid-rapid/)
- [AnalyticsBot GenAI based platform to streamline decision-making in analytics](/accelerators/sigmoid-analyticsbot/)
- [DataConnect Seamlessly ingest, integrate and harmonize data from diverse sources](/accelerators/sigmoid-dataconnect/)
- [Reconica AI-powered data harmonization and reconciliation engine](/accelerators/sigmoid-reconica/)
- [ConverseBot GenAI driven insights generation for automated insights from reports](/accelerators/#sales|conversebot)
- [iNRM Cross-lever revenue growth optimization platform](/accelerators/sigmoid-inrm/)
- [AssortmentIQ Optimize shelf layouts and assortment mix at scale with AI-based insights](/accelerators/sigmoid-assortmentiq/)
- [Read Whitepaper](/ebooks-whitepapers/building-agentic-ai-chatbots-for-business-process-transformation/)
- [Listen Podcast](/events/podcast/how-jack-in-the-box-is-redefining-personalization-and-supply-chain-with-ai/)
- [Blogs](/blogs/)
- [White Papers](/ebooks-whitepapers/)
- [Case Studies](/case-studies/)
- [Podcast](/events/podcast/#Podcasts)
- [Read Blog](/blogs/the-genai-adoption-triad-responsibility-ethics-and-explainability/)
- [ConverseBot](/accelerators/#sales|conversebot/)
## Tags
- AI/ML
- Cloud Transformation
---
## Footer Links
- [Talk to our AI experts](/contact-us/)
- [AI Strategy](/enterprise-ai-strategy/)
- [Agentic AI](/agentic-ai-solutions/)
- [Generative AI](/generative-ai/)
- [AI Managed Services](/ai-managed-services/)
- [Responsible AI](/responsible-ai-in-enterprise/)
- [Advanced Analytics](/advanced-data-analytics-solutions/)
- [Data Strategy](/data-analytics-strategy//)
- [Data Engineering](/data-engineering/)
- [Data Management](/ai-data-management-services/)
- [Cloud Transformation](/cloud-transformation/)
- [Data Ops](/data-devops/)
- [Data Visualization](/data-visualization-service/)
- [Automated Insights](/automated-insights/)
- [BI Migration](/bi-migration/)
- [Data Modeling](/data-modeling-services/)
- [Data Observability](/data-observability/)
- [CPG & Retail](/industries/cpg-analytics/)
- [Financial Services](/industries/banking-financial-analytics-services/)
- [Life Sciences](/industries/life-sciences/)
- [Case Studies](/case-studies/)
- [Thought Leadership](/ebooks-whitepapers/)
- [Blogs](/blogs/)
- [Company](/about-sigmoid/)
- [Newsroom](/newsroom/)
- [Accelerators](/accelerators/)
- [Careers](/careers/)
- [Privacy Policy |](/privacy-policy/)
- [Cookie Policy](/cookie-policy/)