Find a job 
Featured Companies 
Featured Universities 
For Companies At a Glance
- Tasks: Design and maintain scalable data pipelines for reliable analytics and decision-making.
- Company: Keyrus, a global leader in Data Intelligence with a focus on sustainability.
- Benefits: Hybrid work model, competitive salary, medical insurance, and training opportunities.
- Other info: Inclusive workplace that values diversity and innovation.
- Why this job: Join a dynamic team and make a real impact with data-driven solutions.
- Qualifications: 4+ years in data engineering, strong Python and SQL skills required.
About Keyrus
Keyrus is a global consulting and technology company focused on making data matter — truly matter — from a human perspective. Founded in 1996, Keyrus operates in 28+ countries across 5 continents, with more than 3,300 people worldwide. Our strength comes from combining deep expertise in Data & Analytics, AI, Digital, and Management Consulting with a strong understanding of business realities. Data is never the goal in itself. We use data to shape understanding, design meaningful experiences, and enable better, real-life decisions. At Keyrus, we also believe companies have a responsibility beyond performance. Through our Foundation and ESG initiatives, we actively contribute to sustainability, inclusion, and positive societal impact.
The Role
As a Data Engineer, you will work at the intersection of data, technology, and business, helping design and scale modern data platforms that enable reliable analytics and informed decision-making. You will be part of an agile, high-performing team focused on building and maintaining data pipelines, working with large and complex datasets, and ensuring data is accessible, accurate, and performant across the organisation.
Your Impact
- Design, build, and maintain scalable data pipelines to ingest, transform, and deliver data from multiple sources
- Work with large and complex datasets, ensuring they are clean, accurate, and ready for analytics
- Collaborate with analysts and data scientists to make data accessible and useful
- Develop data processing solutions using Python (pandas, PySpark) and SQL
- Optimize queries and pipelines to improve performance and efficiency
- Ensure data quality, reliability, and security across all workflows
- Automate repetitive tasks and continuously improve data engineering processes
- Contribute to data platform stability and scalability in a distributed environment
What Makes This Role Challenging
- You will work with large-scale, complex data ecosystems
- You must balance performance, scalability, and reliability
- You will collaborate across multiple teams with evolving data needs
- You will contribute to systems supporting critical business and analytical use cases
What We’re Looking For
Must-haves- 4+ years of experience in data engineering or related roles
- Strong proficiency in Python (pandas, PySpark) and SQL
- Hands-on experience building data pipelines using Airflow or similar tools
- Experience with cloud platforms (AWS, Azure, or GCP)
- Solid knowledge of Linux/Unix environments and basic scripting (Bash)
- Understanding of data modelling concepts and performance optimisation
- Experience with CI/CD pipelines and DevOps practices
- Familiarity with Agile methodologies
- Strong analytical mindset with a focus on data quality and continuous improvement
- Ability to communicate clearly with both technical and non-technical stakeholders
- Fluency in English
- Experience with streaming technologies (e.g., Kafka or similar)
- Exposure to modern data platforms such as Databricks or Snowflake
- Experience with NoSQL databases
- Knowledge of data governance and data security best practices
- Interest or experience in real-time analytics or AI/ML data pipelines
- Exposure to large-scale distributed data systems
How Our Salary Ranges Work
At Keyrus, salary ranges reflect different levels of mastery and impact within the same role — not different job titles. Final offers are based on experience, autonomy, scope, and market context, and are discussed transparently during the process.
What We Offer
- Hybrid work model (3 days/week in office)
- 18 paid leaves + 9 public holidays
- ₹5L medical insurance + ₹10L accident insurance
- Competitive fixed salary with EPF & statutory benefits
- International consulting exposure with Keyrus clients
- Discretionary Bonus Scheme
- Referral Bonus Scheme
- Training & Development via KLX (Keyrus Learning Experience)
Why Keyrus?
Joining Keyrus means joining a market leader in Data Intelligence, a company where people, trust, and diversity are core values, and an environment that values ownership, flexibility, and innovation. We believe diversity drives better thinking, stronger teams, and better outcomes. Everyone belongs at Keyrus.
Equal Opportunity Statement
We are committed to building an inclusive workplace and encourage applications from all backgrounds, regardless of race, ethnicity, gender identity, sexual orientation, age, disability, or any other protected characteristic.
Data Engineer Bangalore · employer: The Keyrus Group
Contact Detail:
The Keyrus Group Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Data Engineer Bangalore ·
✨Tip Number 1
Network like a pro! Reach out to current or former employees at Keyrus on LinkedIn. A friendly chat can give you insider info and maybe even a referral, which can really boost your chances.
✨Tip Number 2
Prepare for the interview by brushing up on your technical skills. Since you'll be working with Python and SQL, make sure you can talk about your past projects and how you've tackled data challenges.
✨Tip Number 3
Show your passion for data! During interviews, share examples of how you've used data to drive decisions or improve processes. This will demonstrate that you understand the importance of data in real-life scenarios.
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, it shows you're serious about joining the Keyrus team.
We think you need these skills to ace Data Engineer Bangalore ·
Some tips for your application 🫡
Tailor Your CV: Make sure your CV reflects the skills and experiences that match the Data Engineer role. Highlight your proficiency in Python, SQL, and any relevant data engineering projects you've worked on. We want to see how you can make data matter!
Craft a Compelling Cover Letter: Your cover letter is your chance to show us your personality and passion for data engineering. Share why you're excited about working at Keyrus and how you can contribute to our mission of making data meaningful. Keep it concise but impactful!
Showcase Your Projects: If you've worked on any interesting data projects, don’t hesitate to mention them! Whether it's building data pipelines or optimising queries, we love to see real examples of your work. It helps us understand your hands-on experience better.
Apply Through Our Website: We encourage you to submit your application through our website. It’s the best way to ensure your CV gets into the right hands. Plus, it shows us you're serious about joining our team at Keyrus!
How to prepare for a job interview at The Keyrus Group
✨Know Your Data Tools
Make sure you brush up on your Python, SQL, and any data pipeline tools like Airflow. Be ready to discuss specific projects where you've used these technologies, as well as any challenges you faced and how you overcame them.
✨Understand the Business Impact
Keyrus focuses on making data matter from a human perspective. Prepare to explain how your work as a Data Engineer can influence business decisions and improve processes. Think about examples where your data solutions led to tangible outcomes.
✨Showcase Your Collaboration Skills
Since you'll be working with analysts and data scientists, be prepared to talk about your experience collaborating across teams. Highlight instances where you communicated complex data concepts to non-technical stakeholders effectively.
✨Emphasise Continuous Improvement
Keyrus values innovation and improvement. Share examples of how you've optimised data pipelines or automated tasks in previous roles. Discuss your approach to ensuring data quality and reliability, and how you stay updated with industry best practices.