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.
- AI Data Engineers: Take advantage of our data engineers' proficiency in machine learning, statistics, programming, and much more to build and maintain the infrastructure that AI models need to run efficiently.
- Big Data Engineers: Our experts, skilled in handling massive datasets beyond the scope of traditional data processing methods, are an invaluable addition to your data team.
- Machine Learning Engineers: Enhance your business with high-performing machine-learning models built by the industry's best talents.
- Database Data Engineers: Extend your in-house team with our database data engineers, proficient in SQL, NoSQL databases, and data warehousing to design, build, and maintain databases with no hassle.
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
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
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.
Development
Our team starts building the necessary solutions. We focus on creating robust, scalable, and efficient systems that align with your project requirements.
Deployment
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.
Maintenance
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
People also asked
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.
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.
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.
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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