Lead II - Software Engineering(Hyderabad)

Job Summary

We are seeking a skilled Data Engineer to design, build, and optimize scalable data pipelines and cloud-based data platforms. The role involves working with large-scale batch and real-time data processing systems, collaborating with cross-functional teams, and ensuring data reliability, security, and performance across the data lifecycle.

Key Responsibilities

ETL Pipeline Development & Optimization

1. Design, develop, and maintain complex end-to-end ETL pipelines for large-scale data ingestion and processing.
2. Optimize data pipelines for performance, scalability, fault tolerance, and reliability.

Big Data Processing

1. Develop and optimize batch and real-time data processing solutions using Apache Spark (PySpark/Scala) and Apache Kafka.
2. Ensure fault-tolerant, scalable, and high-performance data processing systems.

Cloud Infrastructure Development

1. Build and manage scalable, cloud-native data infrastructure on AWS.
2. Design resilient and cost-efficient data pipelines adaptable to varying data volume and formats.

Real-Time & Batch Data Integration

1. Enable seamless ingestion and processing of real-time streaming and batch data sources (e.g., AWS MSK).
2. Ensure consistency, data quality, and a unified view across multiple data sources and formats.

Data Analysis & Insights

1. Partner with business teams and data scientists to understand data requirements.
2. Perform in-depth data analysis to identify trends, patterns, and anomalies.
3. Deliver high-quality datasets and present actionable insights to stakeholders.

CI/CD & Automation

1. Implement and maintain CI/CD pipelines using Jenkins or similar tools.
2. Automate testing, deployment, and monitoring to ensure smooth production releases.

Data Security & Compliance

1. Collaborate with security teams to ensure compliance with organizational and regulatory standards (e.g., GDPR, HIPAA).
2. Implement data governance practices ensuring data integrity, security, and traceability.

Troubleshooting & Performance Tuning

1. Identify and resolve performance bottlenecks in data pipelines.
2. Apply best practices for monitoring, tuning, and optimizing data ingestion and storage.

Collaboration & Cross-Functional Work

1. Work closely with engineers, data scientists, product managers, and business stakeholders.
2. Participate in agile ceremonies, sprint planning, and architectural discussions.

Skills & Qualifications

Mandatory (Must-Have) Skills

1. AWS Expertise
2. Hands-on experience with AWS Big Data services such as EMR, Managed Apache Airflow, Glue, S3, DMS, MSK, and EC2.
3. Strong understanding of cloud-native data architectures.
4. Big Data Technologies
5. Proficiency in PySpark or Scala Spark and SQL for large-scale data transformation and analysis.
6. Experience with Apache Spark and Apache Kafka in production environments.
7. Data Frameworks
8. Strong knowledge of Spark DataFrames and Datasets.
9. ETL Pipeline Development
10. Proven experience in building scalable and reliable ETL pipelines for both batch and real-time data processing.
11. Database Modeling & Data Warehousing
12. Expertise in designing scalable data models for OLAP and OLTP systems.
13. Data Analysis & Insights
14. Ability to perform complex data analysis and extract actionable business insights.
15. Strong analytical and problem-solving skills with a data-driven mindset.
16. CI/CD & Automation
17. Basic to intermediate experience with CI/CD pipelines using Jenkins or similar tools.
18. Familiarity with automated testing and deployment workflows.

Good-to-Have (Preferred) Skills

1. Knowledge of Java for data processing applications.
2. Experience with NoSQL databases (e.g., DynamoDB, Cassandra, MongoDB).
3. Familiarity with data governance frameworks and compliance tooling.
4. Experience with monitoring and observability tools such as AWS CloudWatch, Splunk, or Dynatrace.
5. Exposure to cost optimization strategies for large-scale cloud data platforms.

Skills

big data,scala spark,apache spark,etl pipeline development,