Key Responsibilities

Design, develop, and maintain ETL/ELT pipelines for structured and unstructured data

- Build scalable data architectures including data lakes, data warehouses, and real-time streaming platforms
- Ingest and integrate data from multiple internal and external sources
- Perform data transformation, cleansing, and enrichment to ensure high data quality
- Ensure data availability, reliability, and performance across systems
- Maintain data lineage, metadata, and documentation for audit and compliance purposes
- Support integration of data pipelines with AI/ML model training and deployment workflows
- Ensure compliance with regulatory requirements and enterprise data policies
- Support data migration and modernization initiatives
- Collaborate with DevOps for deployment, automation, and CI/CD of data pipelines.

Required Experience

Mentioned years of experience in data engineering, data platform, or big data roles; Strong experience in building and managing ETL/ELT pipelines and data architectures;

Experience with relational and NoSQL databases;

Experience with big data and distributed processing frameworks;

Experience with cloud data platforms (Azure, AWS,



GCP); Exposure to real-time data streaming and event-driven architectures;

Experience supporting AI/ML pipelines and analytics workloads is highly preferred;

Experience in regulated industries such as banking or financial services is an advantage.

Required Skills

Data Engineering, ETL/ELT, Data Modeling, SQL, Python/Scala, Big Data Processing (Spark/Hadoop), Data Warehousing, Data Lakes, Streaming (Kafka/Event Hubs), Cloud Data Platforms, Data Governance, Data Quality, Performance Optimization, Problem Solving, Debugging, Integration Design, Collaboration

Preferred Tools And Certifications

Data Platforms: Azure Data Factory, AWS Glue, Google Dataflow, Databricks

- Processing: Apache Spark, Hadoop
- Databases: SQL Server, PostgreSQL, MySQL, Snowflake, BigQuery
- Streaming: Kafka, Azure Event Hubs
- Programming: Python, Scala
- Orchestration: Airflow
- Version Control: Git
- DevOps: CI/CD tools (Azure DevOps, Jenkins)
- Monitoring: Azure Monitor, Prometheus, Grafana;

Certifications: Azure Data Engineer Associate, AWS Data Analytics, Google Qualified Data Engineer, Databricks certification (preferred)

📌 Senior Software Engineer (Hyderabad)
🏢 Virtusa
📍 Hyderabad