Data Engineer (Azure/AWS/GCP)

We are seeking skilled Data Engineers with strong expertise in any one major cloud platformAzure, AWS, or Google Cloud Platform—along with modern data engineering practices. In this role, you will design, build, and optimize cloud-native data pipelines and solutions that deliver timely, trusted, and actionable insights. You will play a key role in shaping our data ecosystem, ensuring performance, reliability, and governance throughout the data lifecycle.

Key Responsibilities

• Design, build, and manage modern data pipelines on Azure or AWS or GCP, using native cloud data services (e.g., Azure Data Factory/Synapse/Databricks, AWS Glue/Redshift/EMR, GCP Dataflow/BigQuery/Dataproc).
• Develop scalable ETL/ELT workflows and integrate structured and unstructured data from diverse sources.
• Implement data quality, security, lineage, and governance best practices across pipelines.
• Optimize data processing for performance, scalability, and cost efficiency.
• Collaborate with architects, data scientists, analysts, and product teams to enable advanced analytics and AI/ML use cases.
• Use CI/CD and DevOps practices to automate deployment, testing, and monitoring of data solutions.
• Mentor junior engineers, promote engineering excellence, and contribute to continuous improvement initiatives.

Required Skills & Experience

• 5 –10 years of experience in Data Engineering with hands-on expertise in one of the following cloud ecosystems:
• Azure Data Factory, Synapse, Databricks, Data Lake, Azure SQL
• AWS Glue, Redshift, EMR, S3, Lambda
• GCP BigQuery, Dataflow, Dataproc, Cloud Storage
• Strong skills in data modeling, SQL, Python, PySpark, and data file formats (Parquet/Delta/ORC).
• Experience with CI/CD (Azure DevOps/GitHub Actions/CodePipeline/Cloud Build) and Infrastructure as Code (Terraform/ARM/CloudFormation/Bicep).
• Solid understanding of data governance, security, metadata, and performance optimization practices.
• Experience with real-time streaming tools such as Kafka, Kinesis, Event Hubs, or Pub/Sub.
• Familiarity with modern data architectures: Data Lakehouse, Medallion Architecture, ELT-first design.

Good to Have

• Experience with AI/ML data pipelines or MLOps tools (Azure ML, SageMaker, Vertex AI).
• Knowledge of BI and visualization tools (Power BI, QuickSight, Looker).
• Exposure to multi-cloud or hybrid data integration.
• Experience with data cataloging solutions (Purview, Glue Data Catalog, GCP Data Catalog).