Hire Top Data Engineers
for Your Next Project

Empower your company with Scalo’s seasoned Data Engineers, ready to optimize your data landscape.

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Our Data Engineers' Capabilities 

Data Infrastructure and Architecture
Develop robust and scalable data architectures that support your growing data needs, ensuring efficient data storage, processing, and retrieval.
 Data Integration and ETL
Seamlessly integrate data from multiple sources, employing ETL (Extract, Transform, Load) processes to ensure data is clean, consistent, and ready for analysis.
 Real-time Data Processing
Harness the power of big data technologies to process and analyze large datasets in real time, unlocking valuable insights and opportunities for your business.
 Data Warehousing and Business Intelligence
Build comprehensive data warehousing solutions that consolidate your data, paired with business intelligence tools to enable data-driven decision-making.
 Data Governance and Quality
Implement data governance frameworks to manage data access, compliance, and quality, ensuring your data remains accurate, secure, and trustworthy.

Why Choose Scalo's Data Engineers? 

Scalo Ui Ux consulting
  • Access to over 1700 data engineers: Scalo has over 10% of the Polish data engineers in it’s direct talent pool, with the aim to increase this percentage to 20% of the market in 2024.
  • On-demand Talent Pool: Access a diverse team of data engineers from Europe, handpicked for their technical proficiency and problem-solving skills.
  • Flexible Engagement Models: Whether you need a dedicated team, project-based support, or individual data engineering experts, we offer flexible engagement models to meet your specific requirements.
  • Commitment to Quality: Our data engineers adhere to the highest standards of quality and best practices in data engineering, ensuring your data solutions are robust, scalable, and delivered on time.
  • Transparent Communication: Stay informed at every stage of your project with clear, regular updates. Excellent English guaranteed, facilitating seamless collaboration and understanding.
Hire Seasoned Data Engineers Today
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Let's build your Perfect Data Engineering Team

Assess the problem
Align the engagement, its justification, and the bigger picture​.
Establish the scope & define the ideal team
Define the required capabilities and skill set. Parametrize and define target team.
Augment the organization
Expand your capabilities with your ideal team or extend the collaboration with consulting service​.

Tech Arsenal
 of Our Data Engineers 

Programming Languages and Frameworks
Expertise in a variety of programming languages such as Python, Scala, and Java, and frameworks like Apache Hadoop and Spark, essential for efficient data processing and analytics.
 Database Management Systems
Mastery of relational (SQL) and non-relational (NoSQL) database management systems, including MySQL, PostgreSQL, MongoDB, and Cassandra, to store and manage data effectively.
 Data Visualization Tools
Proficiency with data visualization tools and libraries such as Tableau, Power BI, and D3.js, enabling the creation of intuitive dashboards and reports for data-driven decision-making.
 Stream Processing Technologies
Experience with stream processing technologies like Apache Kafka and Apache Flink, allowing for the real-time processing and analysis of streaming data.
 Cloud Platforms
In-depth knowledge of cloud platforms such as AWS, Google Cloud Platform, and Microsoft Azure, which provide scalable and flexible data storage, processing, and analytics services.
 Data Orchestration Tools
Familiarity with data orchestration tools like Apache Airflow and Luigi, crucial for automating and managing complex data workflows and ETL processes.
 Machine Learning Libraries
Competence in utilizing machine learning libraries and frameworks, including TensorFlow, PyTorch, and scikit-learn, for building predictive models and AI-driven data analysis.
 Containerization and Virtualization
Skill in using containerization and virtualization tools such as Docker and Kubernetes, facilitating the deployment and scaling of data applications in diverse environments.
 Data Security and Encryption Technologies
Dedication to implementing advanced data security measures, including encryption technologies and secure data access protocols, to protect sensitive information and ensure compliance.

AI Implementation

Lars M., CTO, European FinTech company

In the fast-paced world of Fintech, having a robust and scalable e-commerce platform is pivotal. Our collaboration with Scalo’s Data Engineering team has been nothing short of transformative for our platform and, by extension, our business. From the initial stages of conceptualization to the final deployment, the expertise and meticulous attention to detail exhibited by the team were exemplary.

They not only optimized our data architecture, making it more efficient and scalable but also enhanced data processing and analytics capabilities, ensuring data integrity and insightful reporting. The team’s profound skills in managing and analyzing data have significantly improved our platform’s performance, providing our customers with a seamless and enriched user experience.

Scalo has truly been a catalyst in propelling our platform to new heights in the competitive Fintech arena, leveraging the power of advanced data engineering to drive innovation and growth.

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Build your Perfect Data Team 

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Scalo in Numbers 

600+

Software experts​

1

Week to get first talents

750+

Projects delivered

70+

Clients

Our Recognitions
Microsoft Gold dark
ISO 27001
clutch
Gazele biznesu
PMI
Microsoft partner
abst
clutch
computerworld
ISO 27001

Our Expertise in Data & AI Technologies

Governance

Make sure your software meets regulators’ requirements

GDPR and Data Protection Laws
Adherence to General Data Protection Regulation requirements and other global data protection laws, ensuring the privacy and security of personal data.
 Data Governance Framework
Implementation of a comprehensive data governance framework to manage data access, quality, and lifecycle, aligning with organizational policies and standards.
Data Quality Standards
Adoption of industry best practices for data quality, ensuring that data is accurate, complete, and reliable for decision-making processes.
 Data Security and Privacy
Enforcement of robust data security measures and privacy protocols to protect sensitive information from unauthorized access and breaches.

Skills

Data Governance and Management
Expertise in establishing and maintaining the policies and practices that ensure data quality, security, and accessibility across the organization. This includes managing both batch and real-time data workflows, ensuring data integrity, and compliance with data protection regulations.
 Proficiency with SQL and NoSQL Databases
Skilled in working with various SQL databases like MySQL and PostgreSQL, as well as NoSQL databases such as MongoDB and Cassandra. This includes designing, implementing, and managing databases to support application requirements.
 Data Pipeline Development
Ability to develop and maintain robust data pipelines using tools like Apache Airflow for batch processes and Apache Kafka for real-time data streaming. This skill is crucial for the efficient movement, filtering, and processing of data from various sources.
 Cloud Platform Integration
Experienced in deploying and managing data solutions across major cloud platforms, including AWS, Azure, and GCP. This includes setting up cloud data warehouses, data lakes, and leveraging cloud-native services for data processing and analytics.
 DataOps and CI/CD Implementation
Advanced skills in DataOps practices and continuous integration and deployment methodologies, ensuring that data pipelines are scalable, reliable, and efficiently updated in response to changing requirements.
 Big Data Technologies
Proficiency in handling big data technologies and analytical engines like Apache Spark, Hadoop, and Databricks, enabling the processing and analysis of large datasets to extract insights and support data-driven decision-making.
 Data Security and Compliance
Knowledge of implementing data security measures, including encryption, access controls, and compliance with standards such as GDPR, PCI-DSS, and ISO-27001, to protect sensitive information and ensure data privacy.
 Advanced Analytics and Machine Learning
Familiarity with utilizing machine learning libraries and frameworks to develop predictive models and analytics solutions. This includes using Python packages like pandas, TensorFlow, and scikit-learn for data analysis and model building.
 Containerization and Orchestration
Expertise in using containerization technologies like Docker and Kubernetes to create scalable, efficient, and isolated environments for data applications, ensuring consistent performance across different deployment environments.
pm services scalo

Use Case 1: Customer Data Platform Implementation

Scenario: A retail company is looking to improve its customer engagement and personalization strategies by implementing a Customer Data Platform (CDP). They need to integrate data from various sources, including online transactions, customer feedback, and in-store purchases, to create a unified customer view.

How Scalo’s Data Engineers Can Help:

  • Data Integration and Governance: Regular data engineers from Scalo can manage the integration of diverse data sources into a unified platform, ensuring data quality and governance throughout the process.
  • Batch Data Management: Utilizing technologies like Apache Spark and Databricks, they can develop batch data pipelines to process and consolidate historical customer data efficiently.
  • Data Model Development: By creating efficient data models, Scalo’s engineers can facilitate the analysis and segmentation of customer data, enabling targeted marketing and personalized customer experiences.

Use Case 2: Real-Time Analytics for Financial Services

Scenario: A financial services firm seeks to offer real-time analytics and fraud detection to its customers. This requires processing and analyzing large volumes of transactional data in real-time to identify potential fraud and alert customers immediately.

How Scalo’s Data Engineers Can Help:

  • Real-Time Data Management: Senior data engineers with expertise in real-time data flows can leverage Apache Kafka and other real-time technologies to manage the continuous stream of transaction data.
  • DataOps and CI/CD: Implementing DataOps practices and CI/CD pipelines, they can ensure that the data processing infrastructure is scalable, reliable, and up-to-date with the latest fraud detection algorithms.
  • Containerization: Utilizing Kubernetes, Scalo’s engineers can deploy and manage scalable, efficient data processing environments that can handle spikes in data volume without compromising performance.

Use Case 3: Cloud Data Warehouse Modernization

Scenario: An enterprise is looking to modernize its data warehouse to enhance data analytics capabilities, improve scalability, and reduce operational costs. The goal is to migrate from an on-premises data storage solution to a cloud-based platform.

How Scalo’s Data Engineers Can Help:

  • Cloud Migration Strategy: Senior data engineers can lead the strategy and execution of migrating the existing data warehouse to a cloud platform like AWS Redshift or Azure Synapse, ensuring minimal downtime and data integrity.
  • Data Lake Management: By setting up a data lake in the cloud, they can manage structured and unstructured data, providing a flexible environment for advanced analytics.
  • Serverless Computing: Leveraging serverless technologies such as AWS Lambda or Azure Functions, Scalo’s data engineers can automate data processing tasks, reducing operational costs and improving scalability.
Scalo Data Experts

Enhancing Fintech Operations with Azure: A 60% Cost Reduction

For a US-based fintech provider specializing in post-trade operations, the transformation of their financial data management system was pivotal.

Leveraging React, Python, and Microsoft Azure, the project, running from March to December 2023, focused on enhancing their operational platform for better financial workflow management.

The migration to Azure cloud improved speed, reliability, and scalability, while a visual overhaul simplified complex post-trade data handling.

This modernization not only automated data-sharing but also cut down infrastructure costs by 60%, significantly elevating the trading experience.

Schedule
Schedule
Schedule
an Data Management Workshop

Dominik Żuk

Data & AI Practice Leader

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