Senior Data Engineer

GlobalNodes; Gurgaon; On-site; INR 1,500,000 - 2,100,000; Full time

Job summary

GlobalNodes is seeking a Senior Data Engineer with 3-5 years of experience to design and implement scalable data pipelines and cloud-native data platforms. You will collaborate with Product Managers, Data Scientists, and Software Engineers to enable analytics, BI, and AI-driven solutions.

The role emphasizes building reliable data solutions, data modeling, and governance, with AWS-based data platforms and Spark processing at scale. Gurgaon-based team; on-site collaboration expected.

Qualifications

• 3-5 years of professional experience in Data Engineering.
• Strong programming skills in Python.
• Advanced proficiency in SQL with complex queries.
• Hands-on experience with Apache Spark (PySpark).
• Experience building scalable ETL/ELT pipelines for production environments.
• Experience with AWS services (S3, Glue, Athena, Redshift, Lambda, EMR).
• Experience with relational databases such as PostgreSQL or MySQL.
• Familiarity with Git and CI/CD workflows.
• Knowledge of data modeling and data warehousing concepts.
• Excellent communication and problem-solving skills.

Responsibilities

• Design, develop, and maintain scalable ETL/ELT pipelines for structured and semi-structured data.
• Build and optimize batch and near real-time data processing workflows.
• Process large-scale datasets efficiently while ensuring reliability and scalability.
• Monitor and optimize pipeline performance and data availability.
• Design and maintain efficient data models for analytical and operational use cases.
• Develop and optimize data warehouse solutions.
• Work with relational and NoSQL databases to manage large-scale datasets.
• Build cloud-native data solutions using AWS services.
• Develop distributed data processing applications using Apache Spark (PySpark).
• Integrate data from APIs, databases, files, and streaming sources.

Job description

We are looking for a Senior Data Engineer with 3-5 years of experience to join our Engineering team. The ideal candidate will have strong expertise in designing and developing scalable data pipelines, building cloud-native data platforms, and enabling analytics and AI-driven solutions.

You will work closely with Product Managers, Data Scientists, and Software Engineers to build reliable, high-performance data solutions that support business intelligence, reporting, and machine learning initiatives.

Responsibilities

• Design, develop, and maintain scalable ETL/ELT pipelines for structured and semi-structured data
• Build and optimize batch and near real-time data processing workflows
• Process large-scale datasets efficiently while ensuring reliability and scalability
• Monitor and optimize pipeline performance and data availability

Data Modeling & Storage

• Design and maintain efficient data models for analytical and operational use cases
• Develop and optimize data warehouse solutions
• Work with relational and NoSQL databases to manage large-scale datasets
• Optimize queries and storage for performance and scalability
• Build cloud-native data solutions using AWS services
• Develop distributed data processing applications using Apache Spark (PySpark)
• Integrate data from APIs, databases, files, and streaming sources
• Ensure secure, scalable, and reliable data architectures

Data Quality & Governance

• Implement automated data validation and quality checks
• Monitor production pipelines and troubleshoot data issues
• Ensure compliance with data governance, security, and privacy standards
• Maintain documentation and data lineage for engineering solutions
• Collaborate with cross-functional teams to translate business requirements into scalable technical solutions
• Participate in architecture discussions, code reviews, and technical planning
• Mentor junior engineers and promote engineering best practices
• Contribute to continuous improvement initiatives across the data platform

Requirements

• Bachelor's degree in Computer Science, Information Technology, Engineering, or a related field
• 3-5 years of professional experience in Data Engineering
• Strong programming skills in Python
• Advanced proficiency in SQL with experience optimizing complex queries
• Hands-on experience with Apache Spark (PySpark)
• Experience building scalable ETL/ELT pipelines for production environments
• Strong understanding of data modeling and data warehousing concepts
• Experience with AWS services such as S3, Glue, Athena, Redshift, Lambda, or EMR
• Experience working with relational databases such as PostgreSQL or MySQL
• Familiarity with Git and CI/CD workflows
• Strong analytical and problem-solving skills
• Excellent communication and interpersonal skills
• High level of ownership and accountability
• Ability to work independently in a fast-paced environment
• Strong collaboration and stakeholder management skills
• Continuous learning mindset and passion for modern data technologies

Nice to Have

• Hands-on experience with Databricks for developing and managing data engineering workloads
• Experience with workflow orchestration tools such as Apache Airflow
• Knowledge of Apache Kafka or other streaming technologies
• Experience with Docker and containerized deployments
• Familiarity with Infrastructure as Code (Terraform or CloudFormation)
• Exposure to Delta Lake, Snowflake, or Redshift
• Experience supporting AI/ML data pipelines

We help companies move from AI-curious to AI-powered by building systems that do outcome-based work inside the enterprise.