IT Outstaffing Services      Data Engineering Teams

Scale with Data Engineering Teams

Without a comprehensive data management strategy, data is often underutilised or mismanaged, leading to suboptimal outcomes. At Altamira, we make data work for you. We promote the importance of data as the foundation for any AI and analytical tool implementation. Our team focuses on practical and secure solutions for storing, processing, analysing, and modelling your data while ensuring it is primed to drive your business forward, helping you scale and sustain.

Our expertise

Foster a dynamic and adaptable data strategy, enabling businesses to stay agile and responsive to industry trends.

Altamira is trusted by

Expand and reinforce your team's capabilities, bringing specialised skills and innovative solutions directly to your projects

Key enablers in data engineering

Cloud-based Solutions

The adoption of cloud-based platforms in data engineering reshapes the way data is stored, processed, and accessed. Cloud solutions offer exceptional scalability, allowing data engineers to manage vast amounts of data without the limitations of on-premises infrastructure. They provide flexibility in resource utilisation, where services can be scaled up or down based on demand, leading to cost efficiency.

Data Warehouse

Specialised for structured data, data warehouses offer optimised storage, retrieval, and analysis capabilities. They are essential for businesses that need reliable platforms for business intelligence, reporting, and data consolidation, ensuring data integrity and consistency.

Data Lakehouse

Combining the best features of data lakes and data warehouses, lakehouses enable efficient data management and analytics. We provide scalable storage solutions and cutting-edge analytics, perfect for businesses eager to exploit big data and AI insights.


Integration is a cornerstone of effective data engineering. It involves combining data from various sources into a functional ecosystem while ensuring that the data is harmonised and standardised for consistent analysis. Effective integration strategies enable a unified view of data, making it more accessible for decision-making.


Automation in data engineering streamlines repetitive and time-consuming tasks, such as data extraction, transformation, and loading (ETL). Automation tools and scripts can also handle data monitoring and quality checks, ensuring data integrity and reliability. This shift towards automation leads to faster, more efficient data pipelines, ultimately speeding up business time-to-insight.

Benefits of outstaffing our data engineers

Increased efficiency

With seasoned data engineers on board, tasks are completed with higher efficacy. Our engineers bring niche skills, enabling them to effectively tackle complex data challenges.

Project flexibility

Scale the team up or down based on project requirements. This adaptability is important for managing varying workloads and meeting changing business needs without the commitment of long-term hires.

Focus on core business functions

With a dedicated outstaffed team handling data engineering tasks, in-house staff can focus more on core business activities. This division of labour allows for better allocation of internal resources towards strategic objectives.

Quality improvement

External data engineers can introduce new perspectives and solutions, often leading to improvements in the quality of data management and analytics.

Faster time-to-market

The additional resources and expertise provided by an outstaffed team accelerate project timelines, allowing businesses to bring products and services to market more quickly.

Reduced training and onboarding time

When choosing our data engineering outstaffing solution, there is less need for extensive onboarding and training, leading to a quicker project launch.

How we work

Mapping the problem

Our first step is to understand the unique challenges your business faces. We dive deep into your specific data issues and objectives to ensure we provide targeted solutions.

Familiarisation with existing infrastructure

We take the time to get to know your current data systems and processes. This helps us identify how we can integrate our solutions seamlessly with your existing infrastructure.

Suggestion for deliverables

Based on our initial analysis, we create a detailed proposal outlining the deliverables, including a clear action plan, timelines, and expected outcomes tailored to your needs.


Our team starts building the necessary solutions. We focus on creating robust, scalable, and efficient systems that align with your project requirements.


After thorough testing and quality assurance, we deploy the new data solutions into your business environment. Our deployment process is designed to eliminate disruption and ensure a smooth transition.


Post-deployment, we provide ongoing maintenance and support. Our team ensures your data systems are running optimally and adapts to any evolving needs of your business.

Key business needs to consider before outstaffing the data engineering team

01 Scalability requirements

02 Specialised expertise

03 Cost efficiency

04 Project deadlines

05 Competitive edge

06 Risk management

Team extension with our data engineering teams is the best solution when your in-house professionals are not able to handle fluctuating project demands.

You may face some gaps in your team's expertise, particularly in advanced areas like machine learning, big data technologies, or specific programming languages, which outstaffed professionals can fill.

Consider the cost-benefit ratio of outstaffing versus expanding in-house resources, taking into account recruitment, training, and infrastructure costs.

Assess if your current team has the bandwidth to meet critical deadlines. Otherwise, our outstaffing solution can provide the additional workforce to ensure timely project completion.

Our outstaffed experts bring in new perspectives and innovative approaches that keep your company competitive in a rapidly evolving tech landscape.

We help diversify risk, especially in managing large-scale or complex data projects where in-house expertise may be limited.

Case studies

Custom Mobile App for Dog Owners

Mobile App

Modern technology can help owners care for their dogs and keep them safe. GPS tracks and saves dogs’ history for their whole life, easily transfers it to new owners and ensures the security and detectability of the animal.

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Golf Training App Powered by IoT

Software Development

A golf training app that helps players improve their unique golf style. An opportunity for a deep configuration of the swing goal, great visual support, advanced algorithms, and effective feedback makes this project a revolution for golf!

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Point of Sales SaaS Solution


ZempCenter is a multifunctional Point of Sales mobile app that helps retail companies to manage orders, check inventory and stock count, generate employee reports, perform convenient transactions, and see sales reports.

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All-in-one Solution App for Local Businesses

Social Network

A native iOS and Android app that connects neighbours and helps local businesses to grow within local communities. Bestyn includes posts sharing, private chats, stories and built-in editor for their creation, and tools for promoting local businesses.

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Mobile Payment and Virtual Terminal App

Mobile App

The SkipCash mobile payment app is operated in Qatar, where common PoS terminals are not widespread. SkipCash tries to solve this problem with a mobile payment app that replaces the payment terminal on the seller's side and replaces the payment card on the client's side.


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Altamira financial advisors - financial stability - mobile banking - financial management

Slovak National Theater Web Development


We took over the maintenance, continuous improvement, and further development of the website for the most famous and largest Slovak cultural institution – Slovak National Theater.

Read case

People also asked

What is a data engineering team?

A data engineering team structure consists of data engineers who manage large-scale data systems, ensuring efficient data collection, storage, and accessibility for further analysis.

How to set up a data engineering team?

Setting up a data engineering team involves defining objectives, hiring skilled data engineers, assigning clear roles, equipping them with necessary tools, establishing standard processes, and fostering collaboration within the organisation.

Who do data engineers collaborate with?

Data engineers work directly with data scientists, business analysts, IT teams, database administrators, and project managers to ensure efficient data management and alignment with business goals.

Is data engineering just ETL?

No, data engineering team responsibilities is more than just ETL (Extract, Transform, Load). It includes designing data systems, managing big data technologies, ensuring data quality, and maintaining databases, with ETL being one important aspect.

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