Data Engineering Experts

We make your data accessible and usable.

Our data engineering team will assess your business needs and create robust, scalable data pipelines.

Why Data Engineering Service
Why Data Engineering?

Like any engineer – Data Engineers design and build. Our expert data engineers build pipelines to transform your data into an ideal format for your business needs. Pipelines take data from multiple disjointed or separate sources and collect them into a data warehouse or data lake that represents in a uniform way to be the single source of truth for the enterprise data. All reports depend on this data warehouse, so trusting this data is key.

Transform your data into the ideal format for your business needs.
Reap the benefits of data engineering through improved operational efficiency, easier data collection and deployment, and more accurate decision-making capabilities. Additional benefits include:
  • Improves decision-making capabilities of businesses
  • Allows prediction models to enhance user experience
  • Provides the organization leverage to become more data-driven
  • Enables end-to-end assessment of vital decisions
  • Improves quality of data for better operational efficiency
Our Data Engineering Technology Stack
DataSleek data Engineering
Areas of expertise:

Data Integration Tools:

  • Fivetran
  • Stich Data
  • Airbytes
  • Airflow

Data Pipeline Programming skills

  • Python
  • Spark
  • SQL
  • API Endpoints

AWS & Other Technologies

  • AWS Glue
  • S3
  • Azure
  • Lambda
Data Engineering Services We Offer:
Featured Project
Zappotrack project

When Zappotrack found themselves in the need of a complex app for tracking invoices in the hospitality industry, they worked with Data Sleek to build exactly what they needed.

What Our Clients Say

Data Sleek’s commitment is unmatched; truly first class. Their Business Analysts provided an accurate and important Sales & Fulfillment Analysis report which will definitely help us drive some important business decisions.

Ron Peled , SQQUID, CEO
DataSleek-Client-Testimonials

Wonderful experience, team was responsive and deeply knowledgeable, would recommend!

Alex Lee , NUMERADE, Co-Founder
Testimonials
Want to know how to effectively decentralize your data to have a better understanding of your business?
FAQs

What is data engineering?

Data engineering involves building scalable systems to collect, store and analyze data. It is a wide-ranging field, but at its core it is about solving problems related to extracting value from data.That value comes in many forms: insights, revenue, improved customer experience, etc.

Data engineering as a discipline includes several different roles, from data collection and storage to data analysis to building data models. Also known as business intelligence (BI), data engineering is the process of managing and interpreting large amounts of data for a company or organization. Data engineers are responsible for the creation, maintenance and transformation of the systems that collect, store and manage data.

What can data engineering do for my organization?

The information that companies collect has the power to make or break a business. It’s the backbone of the way organizations do business, and it’s a constant driving force behind decision-making.

Data engineering is important for an organization as it helps optimize data and make it more relevant to decision-making. If data doesn’t meet the criteria of being “actionable” then it’s of no use to a company, since it can’t be used to make decisions.

A lot of people think data engineering is something that only tech companies have to worry about. But in reality, every company—no matter what industry—needs to be concerned with their data. It can either be one of your biggest assets or your biggest weakness. A lot of businesses rely on data to drive critical decisions and improve their bottom line, so they need a team that can collect, store, organize, and analyze this information in a way that gives them an advantage over their competitors.

What poor data engineering practices should be avoided?

  • Making incremental updates without deleting original data: This leads to issues like duplication, inconsistency, and erroneous data.
  • Deploying a new code but not following up with it
  • Failing to create data backups

Get more out of your data with our Data Engineering services.
- Let’s talk.