Background

Data Engineering Company

We help startups and SMEs unlock the full potential of data

Apache AirflowApache KafkaApache SparkAWS KinesisAWS Amazon WebservicesGoogle Cloud Platform

Data Engineering Services


We’ll help you save time and resources. Avoid errors, apply best practices, and deploy high-performance Big Data solutions.

Modern Data Architecture Planning

Our experts assess the project you’re planning or review your existing deployment. Discover best practices, assess design trade-offs, and flag potential pitfalls to ensure that your team’s projects are well designed and built.

Implementing Cloud Data Warehousing

Implement modern data architectures with cloud data lake and/or data warehouse. Develop data pipelines 90% faster and significantly reduce the amount of time you spend on data quality processes.

Data Governance & Compliance

Data governance is one of the single most important data initiatives. We help build data governance foundations and set up the proper tools to meet CCPA, GDPR, and other legal policies requirements.

Cloud Data Solutions Costs Optimization

Our experts can help you leverage the scalability of cloud-native data platforms and gain an advantage with flexible pricing, storage, and performance of your data processing.

Hire a dedicated remote team


Craft an end-to-end data processing solution and apply software craftsmanship principles. Discuss Your Project

Scale up with remote engineers


Looking for data engineers to augment your in-house team with expertise, greater capacity and speed? Hire Remote Developers

Engage technology experts


Searching for an expert to consult on your work, audit a system or bridge skills gaps on your team? Request Consultation

Why Outsource Data Engineering?


Data Engineering Expertise

Leverage our expertise at every stage of the process: data collection, data processing and the Extract, Transform and Load (ETL) process, data cleaning and structuring, data visualisation and building predictive models on the top of data.

Time and Cost Savings

Hiring new employees with the proper skills takes too much time and is expensive — that’s assuming you can even find them in a very competitive job market. We have experts who are ready to work on your data engineering project.

Strengthen your team
with big data experts.
Deliver projects faster.

With the Data Engineering company, our team become an integral part of your team. They immerse themselves in your project, maintaining your company culture and working in line with your strategic goals.

Engage quality
tech professionals

We are driven by 15+ years of experience in IT staff augmentation and engineering software solutions.

Fast process:
CVs within 3 days

Time and cost savings - receive first CVs of our specialists and start interviewing candidates.

Risk-free
2-weeks trial

To get rid of any doubts, check the quality of your team with no obligation to pay with 2-weeks trial.

15
years

of experience

40
+

IT Professionals

20
days

to launch a team

Hire a team in
four simple steps

01

Describe
your needs

Tell us your technology requirements and describe your project using our contact form.

02

Schedule meeting
to align on goals

No one-size-fits-all. We always create a unique team augmentation strategy.

03

Approve
your team

Our HR and tech leaders provide you CVs. You approve each candidate who will join your team.

04

Start your project
with remote talent

We seal the deal with a contract and launch your remote team.

It Staff AugmentationKafka ConsultingSoftware Development Team

What You Get
from Engaging Us

Team Meeting

We source experts that meet clients’ requirements

Our remote-first approach allows us to attract the best talent in Central Europe and scale your team on demand

Fast Team Setup

With SoftKraft it is possible to set up a team very quickly. We follow a rigorous process of hiring people, which then helps our clients to save time and money when hiring our dedicated software development team.

Expert-Level Developers

Our clients deserve experts that will help them solve the issues that they have faced. That is why we hire only developers who are ready to grow and upgrade their skills.

Flexibility and Easy Scaling

SoftKraft is always open to keeping things flexible with the team and clients. If your project needs scaling, our developers can help you with extending your product by adding more features.

Stable Teams

Once we hire someone, we tend to stick to them. That's why we don't have unpredicted situations with staff turnovers. This means that our clients don't suffer from unexpected team member substitutes.

Our strengths will make
your business stronger

Ownership

We take project ownership and responsibility for decisions that were taken during the development

Commitment to results

The success of your projects is the only metric that really matters to us

Focus on your success

Our business strategy is simple: if our customers' business is booming, we are growing too

SoftKraft has been a very reliable partner for us. They took over our system infrastructure in a short time and managed to handle it in a professional and reliable way.
JÖRN STAMPEHL
JÖRN STAMPEHL
VP Engineering at ZenGuard, Germany
The team have been excellent to work with as we develop ML capabilities for our platform. They are harnessing the latest technology in ML and AI for our product goals.
JAMIE ENGEL
JAMIE ENGEL
Founder and CEO of Neutopia, Australia
They are well versed in IoT, big data, and machine learning. The team impressed us with their ability to speak at the business strategy level.
MIKE MIKLAVIC
MIKE MIKLAVIC
CTO at TMC Group, USA

Data Engineering Case Studies


Big Data ETL for Real Estate

Using Apache Spark for Big Data processing speeded up investment decision making and provided scalable infrastructure for growing data volumes and machine learning solutionsLearn More →

Java
Spring Boot
Spark
Camel
Big Data
ETL
AWS

Big Data ETL for Real Estate

User Analytics for a Gaming Community

Using Apache Kafka and OpenShift to build a real-time distributed streaming solution enabling gamers to optimize their performance and monetization strategies in a gaming community.Learn More →

Java
Spring Boot
Kafka
DevOps
Kubernetes
OpenShift
AWS

User Analytics for a Gaming Community

BI Tool for Property Projects

Installing Big Data-enabled BI Tool in company IT infrastructure allowed analysts to interactively examine business data in near real time as well as equipped them to make faster and better investment decisionsLearn More →

Java
Spring Boot
Spark
Zeppelin
AWS Redshift
AWS

BI Tool for Property Projects

Content Processing Software for SEO Services at Scale

Using AWS and cloud machine learning services to craft a software application enabling SEO solutions provider to deliver search engine optimization results at lower costs and scale their SEO service business.Learn More →

Java
Golang
AWS Lambda
AWS EC2
IBM Watson
DynamoDB

Content Processing Software for SEO Services at Scale

Frequently Asked Questions (FAQ)


What is the difference between Data Engineering and Data Science?

Data Engineering and Data Science are complimentary.

Data Engineering ensures that data scientists can look at data security and consistently. Data Engineers handle many core elements of Data Science, such as the initial collection of raw data and the process of cleansing, sorting, securing, storing, and moving that data.

Data Science combines computer science, statistics, and mathematics. Data Scientists apply a combination of algorithms, tools, and machine learning techniques ​​like predictive analytic to help you to extract knowledge from the data. ​

What are the key Data Engineering technical skills?

Data Engineers need to know skills and tools like:

Python, Java, and Scala programming languages

Python is the top programming language used for Data Engineering, followed by Java which is widely used in data architecture frameworks (most of their APIs are designed for Java). Scala is an extension of the Java language that simplifies its syntax.

Database systems (SQL and NoSQL)

SQL is the standard programming language for building and managing relational database systems (tables made of rows and columns). NoSQL databases are non-tabular and come in a variety of types depending on their data model, such as a graph or document. Data Engineering utilizes both depending on their pros and cons, see the question What Data Store Should I use? for more details.

Data warehouses

Data warehouses store large volumes of current and historical data. This data is sourced from numerous sources, such as CRMs, ERPs, and accounting software. Data Engineering services help organizations extract knowledge from data through reporting, analytics solutions, and data mining.

Why are Data Engineers more than just experts on one specific technology?

The technologies used by Data Engineering companies use have quickly evolved over past two decades. Nowadays, data processing is more often done with technologies like Apache Spark, Apache Hive, Apache Kafka, and other big data technologies that are running on cloud platforms like Amazon Web Services or Google Cloud Platform.

What are the non-technical skills that are the most valuable for Data Engineers?

Data Engineers need a handful of soft skills to perform their job well:

Communication skills

Data Engineering team will almost certainly interact with a diverse range of stakeholders, many of whom possess varying degrees of technical expertise. Communication skills are critical for effective collaboration.

Collaboration

As critical as communication abilities, Data Engineers must be able to work in teams. Data Engineers need to understand the expectations of Data Science teams with whom they are collaborating, the frequency with which they require updates, and their pain points.

Adaptability

As projects change or evolve, they must be able to reprioritize and adjust. When things do not go according to plan, Data Engineering experts must be able to devise a workaround. Failure to do so may result in frustration, missed deadlines, and resource wastage.

How do you typically deploy a Data Engineering solution?

At a high level, Big Data Engineering has a generic architecture that applies to the majority of businesses:

Data Ingestion

You need your Big Data setup to handle all incoming data streams, whether structured, unstructured, or semi-structured. The incoming data is prioritized and categorized for a smooth flow into further layers down the line. Data ingestion can happen through real-time streaming or batch jobs. We typically use Apache Kafka and AWS/GCP specific solutions (GCP Pub/Sub, GCP Big Query, GCP Cloud Storage, AWS Redshift, AWS S3, AWS Athena, etc) for creating data ingestion pipelines.

Data Storage

After raw data is ingested, the extracted data should be stored somewhere. The storage solution should be in line with the data ingestion requirement of your business ecosystem. Data Storage on AWS by access characteristics:

See question What Data Store Should I use? for more details.

Data Processing

The processing layer where the analytical process begins, where data is needed for analysis is selected, cleaned, formatted for further analysis and modeling. The goal is to discover useful information, suggest conclusions,s and support decision-making. Data Processing on AWS by access characteristics:

Data Visualizations

This layer is everything to do with a graphical representation of information and value gained through analysis. Using rich charts, graphs, and maps, the tools in this layer help present a compelling story for a decision to be made by your leadership team.

We typically use Amazon QuickSignt or Tablau, see our article Embedded Analytics: Amazon QuickSight vs Tableau