Microsoft Azure is a comprehensive cloud solution that enables the building, deployment, and maintenance of data architectures to empower smart decision-making. Here, we’ll discuss what to outsource vs. build in-house for Azure data engineering support in a growing enterprise.
Microsoft Azure is a robust cloud computing platform with a vast and powerful ecosystem, offering over 200 services to meet various requirements, including storage, computing, networking, analytics, artificial intelligence, security, and more. It empowers engineers to build, deploy, implement, and integrate large-scale infrastructure on the cloud. Azure is among the top three cloud providers with a revenue of over $75 billion in 2025.
Businesses across industries, from startups to large enterprises, can benefit from investing in Azure data engineering to streamline and automate workflows and share real-time insights with C-suites and leaders for proactive decision-making. However, a question often arises about whether you should build an in-house team for Azure data engineering support or outsource the responsibility to a third-party offshore service provider. Many organizations in recent times are opting for Azure data engineering managed services from certified companies to have direct access to global talent and expertise and ensure priority support around the clock.
That said, both methods have their advantages and disadvantages, and the choice depends on what is right for your enterprise. In this blog, we’ll find out the responsibilities of a data engineer and compare the pros and cons of outsourcing and building in-house teams for Azure data engineering support.
What is Azure Data Engineering?
Azure data engineering involves a series of processes, such as data mining, data pipeline development, data integration, data transformation, and data analytics. From collecting data from various sources to consolidating it in a central repository and supporting analytics, data engineering ensures raw data is transformed into actionable insights.
These insights are shared as visualization reports with end users (C-level executives, employees, management, investors) for intelligent decision-making. The entire data architecture is hosted on Microsoft Azure cloud with tools and technologies belonging to the same ecosystem. For example, the workflows are created using Data Factory, while Synapse Analytics and Stream Analytics are used for different types of analysis, and the Data Lake Storage is where the vast datasets are stored.
By hiring end-to-end Azure data engineering consulting, CTOs and CIOs can create a strategic plan to build a tailored data architecture that aligns with the business vision and objectives to support data-driven decision-making across all levels. The insights can be used by C-suites, department heads, VPs, team leaders, and other employees in the enterprises, whether in the same office or different branches worldwide.
Responsibilities of an Azure Data Engineer
A data engineer has certain roles and responsibilities, irrespective of whether they are an internal employee or a consulting and managed service provider.
Data Management and Storage
Azure data engineers have to handle data and systems in various forms. This involves selecting the data sources, setting up the connections, building the data storage, and ensuring seamless data flow. Continuous Azure data engineering support ensures that high-quality data is used to derive insights by building robust storage solutions.
Data Pipeline Development and Integration
Azure data pipeline development is another vital responsibility, as it is the key step where the collected data is cleaned, transformed, and loaded into the storage and integrated into the rest of the architecture. The data pipelines can be automated using AI-powered tools to keep the cycle moving continuously and enable real-time insights.
Big Data and Analytics
Data engineers are expected to handle vast datasets with new data being added at high velocity. This requires the use of Azure Databricks and Apache Spark for big data analytics. Data processing workflows are created and automated to support real-time analytics and visualization reports. For this, data engineers have to collaborate with data scientists and data analysts.
Data Governance
Data governance is a part of data management dealing with the security, quality, and availability of datasets. It is a framework of guidelines and regulations to ensure that the data architecture is aligned with global data laws and is built to support high-quality analytics. Authorized access is provided to make data easily available to decision-makers, but it is safe from cyber threats.
What Azure Data Engineering Support Should Be In-House vs Outsourced
Typically, it is recommended to outsource the entire project end-to-end and ask for knowledge transfer through training, mentoring, documentation, etc., as it is a cost-effective solution in the long run. That said, building an in-house team for Azure data platform support has certain benefits, as listed below.
Pros and Cons of In-House Azure Data Engineering Support
Pros:
Full Control
An in-house team has complete control over the data architecture, processes, and systems. This allows CTOs to make quick changes whenever required and to ‘own’ the infrastructure and make intelligent decisions in real-time.
Knowledge and Alignment
An in-house team has more knowledge about the enterprise, making it easy to align the processes with business values and objectives. CTOs, CIOs, and chief data officers can personally ensure proper alignment to derive contextually relevant insights.
Better Data Compliance Measures
When access to data is provided only to employees, management, C-suites, and stakeholders, it simplifies data compliance and reduces the risk of outsiders using confidential data for nefarious purposes.
Stronger Data Security
When there are fewer third-party providers and vendors between your enterprise and Azure data platform services, it reduces the risk of security breaches and weak points that can be targeted by cybercriminals.
Long-Term Investment
An in-house data engineering team is a long-term investment to build a knowledge base in the enterprise, which can be useful to train more employees. CEOs can be confident in their talent and can take calculated risks.
Cons:
Expensive Overheads and Lower ROI
Hiring experienced data engineers is expensive, as there is high demand but not enough talent to meet it. Top management often has to pay premium salaries to retain skilled professionals while also investing heavily in building systems and processes from scratch.
Scalability Issues
Scaling an in-house team can be both costly and time-consuming, especially when workloads fluctuate or business needs change rapidly. The recruitment process itself is lengthy and complex, making it difficult to scale at the pace the business demands.
Talent Retention Challenges
With the growing demand for data engineers, retaining talent over the long term becomes increasingly difficult. Organizations often face ongoing pressure to fill talent gaps, creating stress and potential workflow disruptions.
Limited Access to Talent
Internal teams may lack specialized domain expertise in areas such as AI, big data, and advanced data engineering. This leads to recruitment delays, extended training cycles, and additional operational challenges, ultimately limiting access to the right talent.
Pros and Cons of Outsourced Azure Data Engineering Support
Pros:
Faster Time to Market
With external Azure data engineering support, you can accelerate delivery as these teams are experienced in handling complex projects and come equipped with the right skills, tools, technologies, and domain knowledge to produce results faster.
Access to Global Talent
Outsourcing enables CTOs to collaborate with offshore Azure data engineers and consultants who work remotely on projects. Geographical boundaries no longer limit your ability to build a strong and capable data engineering team.
Greater Scalability and Flexibility
An experienced Azure service provider can scale teams quickly to align with changing business requirements. Specialists can be onboarded on a contract basis, offering greater flexibility while maintaining performance and delivery speed.
Cost-Efficiency and ROI
By eliminating the need to build infrastructure from scratch or hire permanent talent, enterprises can significantly reduce costs associated with building, implementing, and maintaining Azure data architecture for data-driven decision-making.
Focus on Core Functionalities
External teams manage the outsourced Azure projects, allowing internal employees to stay focused on their core responsibilities. This prevents resource dilution and helps the business operate more efficiently.
Implementing Industry Best Practices
Hiring offshore Azure data engineering services provides access to proven industry best practices in data management, engineering, and analytics. This enables better standardization, improved governance, and higher performance benchmarks.
Cons:
Transparency and Dependency
Relying on an outsourcing partner can create dependency on external teams, which becomes a challenge if transparency is lacking. This makes it essential to choose an Azure service provider with a strong reputation, proven processes, and clear communication practices.
Data Security Concerns
Sharing sensitive data with a third party can raise security concerns, even though it is often necessary to build a robust data architecture. Legally binding contracts, NDAs, and strong compliance frameworks help reduce data security risks.
Culture and Communication Barriers
Differences in time zones, culture, and language between in-house and outsourced teams can sometimes lead to delays or misunderstandings. These challenges can be mitigated by working with certified Azure data engineering companies that have experience serving clients within your region and industry.
Conclusion
While in-house Azure data engineers has its advantages, it is a smart move to outsource crucial tasks to experts and use their skills and knowledge in building robust systems. Additionally, with third-party priority support and long-term maintenance, you can free up the internal resources to focus on the core activities and accelerate growth.
Make sure to hire a reputable and certified Azure data engineering managed services provider with industry expertise and transparency. Collaborate with the company to strengthen your systems and internal teams to increase overall efficiency and productivity. Outsourced and managed data engineering services can be a deciding factor in the modern business scenario and establish you as a major contender.
FAQs
Which Azure data engineering tasks are better outsourced than built internally?
Most of the Azure data engineering support tasks are better outsourced than built internally due to the various advantages they offer.
- End-to-end data pipeline development
- Data warehouse development and migration
- Data lake structure and integration
- Big data and real-time analytics
- Monitoring and performance optimization
- Managed services and upgrades
- Security, compliance, and audit
Talk to our certified Azure data engineers to know how tailored services are best for your business to gain a competitive edge and increase ROI.
How do I retain architectural control while using external Azure experts?
Hiring outsourced Azure data engineering support doesn’t mean you handover the entire process and systems to others. You can retain control even when partnering with external talent. Bring experts early into the project, and implement role-based access control to keep sensitive data safe. Additionally, audits should be conducted periodically to monitor for risks and make necessary changes. Our DataToBiz team collaborates with enterprises to offer custom data engineering services with transparency, clear communication, and efficiency.
What cost difference should I expect between in-house and outsourced teams?
Yes, outsourced teams can support legacy systems and cloud-native data pipelines and integrate them with the data architecture to streamline workflows. Our engineers at DataToBiz have the required certification and expertise to provide custom Azure data engineering support to enterprises from diverse industries. We work with on-premises, cloud, and hybrid IT infrastructure.
Can outsourced teams support both legacy and cloud-native data pipelines?
Enterprises can overcome the risk of dependency when implementing external team extension models through knowledge transfer, upskilling, and parallel building internal competencies. Additionally, you can also create a center for excellence (CoE) to leverage the collective talent and promote continuous improvement. With DataToBiz as your IT staff augmentation partner, you can build sustainable teams for the future.
How do I ensure knowledge transfer after external support ends?
When using external Azure data engineering support, it is crucial to build a system for knowledge transfer so that your internal employees can work efficiently and use the dashboards for day-to-day decision-making. Documentation, videos, training, mentoring, etc., are some ways to achieve this. Encouraging team collaborations is also effective. At DataToBiz, we empower our clients by helping them bridge the knowledge gap to increase overall performance.
More in Microsoft Azure Consulting Services Providers
Microsoft Azure consulting services are a range of tailored solutions for infrastructure development, migration, integration, security, compliance, and maintenance of the data architecture on the cloud. Organizations can partner with service providers to benefit from the end-to-end solutions to streamline workflows, optimize internal processes, and share real-time analytical insights with decision-makers. With reliable Azure services, C-suites can empower their employees to be more efficient and productive, thus gaining a competitive edge in the global market.
Check out the links below for more information.
- Azure Data Engineering: The CTO’s Key to Hybrid and Multi-Cloud Wins
- Is Azure Infrastructure as a Service The Future of Cloud Computing?
- Data Engineering Consulting in UAE – 15 Industry Experts to Know
- Top 12 Azure Data Engineering Companies Driving Innovation in Finance
Fact checked by –
Akansha Rani ~ Content Management Executive
