Data science requires close collaboration with data engineering to be effective

Updated on Jul 01, 2022 06:46 PM IST

It's no secret that data science is highly dependent on data engineering. However, this fact is often not appreciated till a problem arises. Through cross-skilling and effective communication, the two teams can deliver better business value to customers

The relationship between data science and data engineering is like the one between an architect and a builder.
The relationship between data science and data engineering is like the one between an architect and a builder.
ByHT Brand Studio

The relationship between data engineering and data science

The reliance on data for businesses across industries like banking, financial services, healthcare, retail, consumer goods, and manufacturing, for strategic decision making is continuing to grow exponentially. However, a recent study highlights that only 30 percent of companies have a well-articulated data strategy while only 29 percent of executives reported achieving transformational business outcomes with data. The primary reason being failure to set up robust data and analytics teams.

The relationship between data science and data engineering is like the one between an architect and a builder. These different skill sets complement each other in critical ways, but they also require a lot of collaboration to work well together.

Model selection and model training account for just about a quarter of the work involved in deploying big data analytics and machine learning applications. Around 50% of the effort goes into getting data ready for analytics and machine learning. The remaining 25% of the effort is spent on making insights easily understood at scale.

Role of a Data Scientist and a Data Engineer

Not long ago, data scientists were expected to perform the role of data engineers as well. But as the field of data analytics has evolved and grown, data management has become more complex, and enterprises are seeking more insights from the data collected, the job has been split into two.

Today, the main difference between these two data professionals is that data engineers build and maintain the systems and structures that store, extract, and organize data, while data scientists analyze that data to predict trends, and glean business insights relevant to the organization.

Increased adoption of the cloud platforms and democratization of data across the organization have spurred sudden demand for data engineering skills. Resultantly, enterprises have had to balance this talent gap through cross-skilling employees and creating a culture of data literacy.

Effective communication between data scientists and data engineers is key to the success of data science projects. Data scientists must be able to understand what data engineers do, and vice versa. For example, data scientists need to understand how their models will be implemented in production environments so they can make sure they're using the right tools and techniques during the analysis phase. Similarly, data engineers need an understanding of what types of problems they'll encounter while implementing the model into their systems so they can design appropriate solutions before starting on any development work.

Lokesh Anand, CEO and Co-founder of Sigmoid, a data engineering and AI solutions company said “Sigmoid takes an interdisciplinary approach to solving our customers’ toughest challenges. We believe the key lies in finding the sweet spot between understanding business problems, data engineering, and analytics. Our data engineers and data scientists work in cohesion to deliver business outcomes, giving them a 360-degree exposure to the life-cycle of data science projects. Thus, in a short period of time our employees get exposure to a wide range of data technologies”.

Sigmoid is building data professionals of the future

Sigmoid combines data and AI engineering to help enterprises gain competitive advantage through effective data-driven decision making. Named as one of the fastest growing companies 2021 in The Americas by Financial Times, Sigmoid specializes in building high-quality data pipelines, enabling cloud transformation, deploying machine learning models into production, and advanced analytics.

Takshashila, an in-house learning academy, started by Sigmoid aims to provide data engineers and data scientists with opportunities to acquire new skills and to enhance their current skills. Takshashila fosters outcome-driven learning, creating multi-skilled data professionals.

"Sigmoid emphasizes on holistic development for its employees through a blended learning approach of instructor-led and online programs by industry experts," said Guchu Nathani, VP of Strategy and Operations at Sigmoid. “Continuous learning is one of our core values and we have a comprehensive learning program that not only focuses on technical skills but also on soft skills that enables our employees to thrive at work," he added.

A majority of data scientists at Sigmoid cross-train themselves on data engineering skills while data engineers gain insights on the ML solution development process. To improve business outcomes for customers, Sigmoid’s data engineers build highly robust and scalable pipelines and analytical sandbox environments, where data scientists get enough room to explore and run experiments for designing tailor made solutions that address specific business problems. The collaborative approach makes it possible for both teams to better understand the requirements, operationalize ML models and boosts the overall productivity.

“Our in-house learning academy, Takshashila, exposes employees to new technologies, domains and best practices. Converting data into meaningful information begins with skilled professionals who are trained on multidisciplinary aspects” said Lokesh.

The employees of Sigmoid get an opportunity to work on solving problems for diverse sectors using a full data technology stack, thus honing their skills. Sigmoid has helped more than 25 Fortune 500 organizations with engineered data solutions that have had millions of dollars in business impact.

  • For a leading multinational consumer goods company, Sigmoid developed a robust MLOps solution to reduce run-time of machine learning model by upto 90% while automating the entire ML pipeline.
  • A Fortune 500 food manufacturing company wanted to build a strong data foundation for enhanced reporting and insights generation. Sigmoid built a centralized data lake automating data with 30+ sources including POS, marketing and financial data in near real-time to generate 2.5x faster insights and reporting.


A recent Gartner report mentions that by 2024, 75% of organizations will have established a centralized data and analytics center of excellence. Data engineers and data scientists work together to wrangle big data and provide insights to business critical decisions. Data scientists use tools like Jupyter Notebooks, Python, and TensorFlow to create models that can have huge impacts on business outcomes. However, these models can only be useful if they use high quality data that is accessible at scale—which is where data engineers come into play. That's why we should strive for constant collaboration between these two teams: working together makes both sides better at their jobs while helping companies achieve their goals more effectively.

Disclaimer: This article has been produced on behalf of the brand by the HT Brand Studio team.

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