Technical Skills:-
1. Core Data Engineering Skills
Big Data Technologies: Proficiency in using GCP's big data tools like:
BigQuery: For data warehousing and SQL analytics.
Dataproc: For running Spark and Hadoop clusters.
Airflow: For pipeline Orchestration
Dataflow: For stream and batch data processing.(High level Idea)
Pub/Sub: For real-time messaging and event ingestion.(High level Idea)
Data Modeling: Experience/Knowledge on designing scalable, efficient data models for OLAP and OLTP use cases.
ETL/ELT Pipelines:
Expertise in building automated, scalable, and reliable pipelines using custom Python/Scala solutions or Cloud Data Fusion.
2. Programming and Scripting
Strong coding skills in Python, SQL, and optionally Java/Scala.
Familiarity with APIs and SDKs for GCP services to build custom data solutions.
3. Cloud Infrastructure
Understanding of GCP services such as Cloud Storage, Compute Engine, and Cloud Functions.
Familiarity with Kubernetes (GKE) and containerisation for deploying data pipelines. (Optional but Good to have)
4. DevOps and CI/CD
Experience setting up CI/CD pipelines using Cloud Build, GitHub Actions, or other tools.
Monitoring and logging tools like Cloud Monitoring and Cloud Logging for production workflows.
Soft Skills: -
1. Innovation and Problem-Solving
Ability to think creatively and design innovative solutions for complex data challenges.
Experience in prototyping and experimenting with cutting-edge GCP tools or third-party integrations.
Strong analytical mindset to transform raw data into actionable insights.
2. Collaboration
Teamwork: Ability to collaborate effectively with data analysts, and business stakeholders.
Communication: Strong verbal and written communication skills to explain technical concepts to non-technical audiences.
3. Adaptability and Continuous Learning
Open to exploring new GCP features and rapidly adapting to changes in cloud technology
1. Core Data Engineering Skills
Big Data Technologies: Proficiency in using GCP's big data tools like:
BigQuery: For data warehousing and SQL analytics.
Dataproc: For running Spark and Hadoop clusters.
Airflow: For pipeline Orchestration
Dataflow: For stream and batch data processing.(High level Idea)
Pub/Sub: For real-time messaging and event ingestion.(High level Idea)
Data Modeling: Experience/Knowledge on designing scalable, efficient data models for OLAP and OLTP use cases.
ETL/ELT Pipelines:
Expertise in building automated, scalable, and reliable pipelines using custom Python/Scala solutions or Cloud Data Fusion.
2. Programming and Scripting
Strong coding skills in Python, SQL, and optionally Java/Scala.
Familiarity with APIs and SDKs for GCP services to build custom data solutions.
3. Cloud Infrastructure
Understanding of GCP services such as Cloud Storage, Compute Engine, and Cloud Functions.
Familiarity with Kubernetes (GKE) and containerisation for deploying data pipelines. (Optional but Good to have)
4. DevOps and CI/CD
Experience setting up CI/CD pipelines using Cloud Build, GitHub Actions, or other tools.
Monitoring and logging tools like Cloud Monitoring and Cloud Logging for production workflows.
Graduate in Computer Science, or related field. 6+ years of experience in data engineering or related field.
Skills Required
- Graduate degree in Computer Science or a related field
- 6+ years of experience in data engineering or a related field
- Proficiency with GCP big data technologies, including BigQuery, Dataproc, Airflow, Dataflow, and Pub/Sub
- Experience designing scalable data models for OLAP and OLTP use cases
- Expertise building automated, scalable, and reliable ETL/ELT pipelines
- Strong Python and SQL coding skills
- Familiarity with GCP APIs and SDKs
- Understanding of Cloud Storage, Compute Engine, and Cloud Functions
- Experience setting up CI/CD pipelines using Cloud Build, GitHub Actions, or similar tools
- Experience with Cloud Monitoring and Cloud Logging
- Java or Scala programming experience
- Familiarity with Kubernetes, GKE, and containerization
What We Do
Choosing a digital partner is about more than capabilities — it’s about collaboration and character. Unrealistic overhauls and off-the-shelf products ignore what matters most — your unique needs, culture, goals, and your legacy data and technology environments. At EXL, our collaboration is built on ongoing listening and learning to adapt our methodologies. We’re your business evolution partner—tailoring solutions that make the most of data to make better business decisions and drive more intelligence into your increasingly digital operations. Whether your goals are scaling the use of AI and digital, redesign operating models, or driving better and faster decisions, we’re here to partner with you to help you gain—and maintain—competitive advantage with efficient, sustainable models at scale. Our expertise in transformation, data science, and change management helps make your business more efficient and effective, improve customer relationships and enhance revenue growth. Instead of focusing on multi-year, resource- and time-intensive platform designs or migrations, we look deeper at your entire value chain to integrate strategies with impact. We use our specialization in analytics, digital interventions, and operations management—alongside deep industry expertise — to deliver solutions that help you outperform the competition. At EXL, it’s all about outcomes—your outcomes—and delivering success on your terms. Share your goals with us and together, we’ll optimize how you leverage data to drive your business forward. For more information, visit www.exlservice.com.
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