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About Kyndryl
Kyndryl is one of the world’s largest IT infrastructure services providers, formed in 2021 as a spin-off from IBM’s Managed Infrastructure Services business. Headquartered in New York, Kyndryl operates in more than 60 countries and partners with thousands of customers across industries including banking, telecommunications, healthcare, manufacturing, and government.
The company specializes in designing, building, managing, and modernizing mission-critical technology systems that are the backbone of the digital economy. Its core services include cloud management, digital workplace solutions, security and resiliency, application modernization, data and AI, and network services. By focusing on open integration and partnerships, Kyndryl works with leading cloud and technology providers such as Microsoft, AWS, Google Cloud, and VMware to deliver tailored solutions for complex IT environments.
What sets Kyndryl apart is its combination of deep technical expertise and global scale. With a workforce of over 80,000 professionals, the company helps organizations accelerate digital transformation, improve resilience, and optimize IT operations.
Kyndryl’s mission is clear: to advance the vital systems that power human progress. By aligning technology with business goals, the company enables enterprises to become more agile, secure, and future-ready in an increasingly interconnected world.
Mastering the Modern Data Engineer Role: Skills, Tools, and Career Insights
As businesses become more data-driven, the role of a Data Engineer has emerged as one of the most crucial and sought-after positions in the tech landscape. Whether it’s supporting real-time analytics, modernizing cloud infrastructure, or building scalable pipelines, today’s Data Engineers are at the core of enterprise innovation.
In this blog post, we’ll explore the essential and preferred qualifications for excelling as a Data Engineer in today’s evolving technology ecosystem. Whether you’re an aspiring professional or an experienced engineer looking to advance your career, understanding these requirements will give you a roadmap to success.
Table of Contents
Core Skills and Experience Required for Success
To succeed as a Data Engineer, a solid technical foundation is essential. Employers today are looking for candidates who can not only manage data but also architect, optimize, and troubleshoot large-scale data systems. Below are the non-negotiable skills and experiences expected from top candidates.
1. Professional Experience in Data Engineering
A minimum of 6+ years of experience in data engineering or database administration is typically required. This experience ensures that the candidate is comfortable navigating complex datasets, managing infrastructure, and implementing data solutions that align with business objectives.
With years of hands-on experience, professionals are expected to have a comprehensive understanding of data ecosystems, including how to handle structured and unstructured data, data integrity, and data security.
2. Data Mining and ETL Expertise
At the heart of every data engineer’s toolkit lies proficiency in data mining, ETL (Extract-Transform-Load) processes, and data storage solutions. Engineers must be adept at:
- Identifying useful data patterns from large datasets
- Extracting data from various sources
- Transforming data to match business requirements
- Loading it efficiently into a data warehouse or data lake
These are foundational capabilities that directly affect the performance of downstream analytics and business intelligence platforms.
3. Mastery of Modern Data Platforms and Tools
The landscape of data engineering tools has evolved significantly. Modern Data Engineers are expected to have hands-on experience with leading platforms and services. Some of the most in-demand tools and platforms include:
- AWS Glue – Serverless ETL tool ideal for building scalable data pipelines in the AWS ecosystem
- Databricks – A unified analytics platform that supports data engineering, machine learning, and collaborative analytics
- Azure Synapse Analytics – A powerful platform that integrates big data and data warehousing for end-to-end analytics
- Google Cloud Dataproc – A fully managed Spark and Hadoop service for processing large datasets
Familiarity with these platforms is crucial for engineers working in cloud-based or hybrid environments.
4. Database Expertise: Relational and NoSQL
Data Engineers must be comfortable working with both relational databases and NoSQL databases to accommodate a wide range of data types and workloads. Common databases in this domain include:
- PostgreSQL – A robust, open-source relational database known for its performance and extensibility
- IBM DB2 – Often used in enterprise environments for mission-critical applications
- MongoDB – A popular NoSQL database known for its flexibility with unstructured data
Knowledge of query optimization, indexing strategies, and schema design is essential for efficient data management and retrieval.
5. Strong Analytical and Communication Skills
Beyond technical prowess, Data Engineers need to exhibit excellent problem-solving, analytical, and critical thinking abilities. Employers look for candidates who approach challenges with a data-driven mindset and can think several steps ahead to design resilient systems.
Equally important are communication skills. A top-tier Data Engineer should be able to:
- Translate complex business requirements into clear technical specifications
- Collaborate effectively with both technical teams (e.g., software developers, data scientists) and non-technical stakeholders (e.g., product managers, executives)
- Document processes, data flows, and system architectures comprehensively
Preferred Skills That Set Candidates Apart
While core qualifications lay the foundation, possessing additional skills and certifications can provide a competitive advantage in today’s job market.
1. Experience in Cloud Modernization and Hybrid Architectures
As more organizations migrate from legacy systems to cloud-based or hybrid infrastructures, prior experience in cloud modernization projects or hybrid data environments is highly valued.
Candidates who can help businesses seamlessly transition to scalable, secure cloud data platforms will stand out significantly.
2. Proficiency in Data Modeling Techniques
A firm grasp of data modeling—including conceptual and logical models—is critical for creating data architectures that align with organizational goals. Understanding entity relationships, normalization, and schema development is essential for ensuring data consistency and supporting business processes.
3. Relevant Certifications
Certifications help validate a candidate’s skills and commitment to ongoing learning. Highly regarded credentials in the data engineering space include:
- Open Certified Technical Specialist (Data Engineering Specialization)
- AWS Certified Data Analytics – Specialty
- Google Cloud Professional Data Engineer
- Microsoft Certified: Azure Data Engineer Associate
- Elastic Certified Engineer
These certifications not only demonstrate proficiency in specific platforms but also show employers a proactive attitude toward professional development.
4. Familiarity with Development and Version Control Tools
Version control and collaborative development tools are essential for modern software and data engineering teams. Experience with platforms like:
- GitHub or GitLab
- Visual Studio Code or Visual Studio
…enables engineers to manage codebases efficiently, conduct peer reviews, and collaborate in agile environments.
5. Educational Background in Technical Fields
A formal education in a technical or scientific field further strengthens a candidate’s foundation. Preferred degrees include:
- Computer Science
- Software Engineering
- Information Technology
- Data Science
Such degrees typically provide exposure to programming, algorithms, data structures, and systems design—all integral parts of a Data Engineer’s role.
Conclusion: Building a Future-Proof Data Engineering Career
The modern Data Engineer must combine deep technical skills with the ability to understand and address business challenges. From mastering ETL tools and cloud platforms to developing strong communication and modeling abilities, the bar is set high—but the rewards are equally significant.
In a world increasingly shaped by data, skilled Data Engineers are the architects of the infrastructure that powers everything from predictive analytics to real-time decision-making. By aligning your expertise with the requirements discussed in this post, you’ll be well-positioned to thrive in this high-demand, high-impact role.
