Job Overview

- Role: Senior Data Engineer

- Location: Gurugram, Haryana

- Experience Required: 4 to 8 Years

- Primary Tech Stack: AWS, PySpark, Advanced SQL

Role Overview

We are looking for a agile and results-driven Data Engineer with strong expertise in AWS, PySpark, and SQL to join our growing technology team in Gurugram. In this role, you will design, build, and optimize large-scale data pipelines, data warehouses, and modern cloud analytics platforms to handle high-volume data workloads.

Key Responsibilities

- Pipeline Development: Design, develop, test, and maintain robust ETL/ELT data pipelines using PySpark and cloud-native services.

- Cloud &
- Big Data Management:

Build, monitor, and optimize scalable data ingestion, transformation, and processing workflows on AWS (e.g., S3, Glue, EMR, Athena, Redshift, Lambda).

- SQL Optimization: Write complex SQL queries, perform query tuning, and manage database operations to ensure high performance and low latency.

- Data Modeling &

- Architecture:

Collaborate with cross-functional teams to build data models, schema designs, and data marts supporting analytical reporting.

- Performance Tuning: Troubleshoot and resolve production performance bottlenecks in distributed data processing jobs.

- Collaboration: Work closely with Data Scientists, Business Analysts,



and DevOps teams to align data platform infrastructure with business requirements.

- Best Practices: Ensure data quality, security, governance, and CI/CD automation standards are implemented across all deliverables.

Required Qualifications &

- Skills

- Experience: 4 to 8 years of hands-on experience in Data Engineering, Big Data, or Business Intelligence roles.

- Programming Languages: Expert-level proficiency in Python and PySpark.

- Cloud Ecosystem: Strong production experience working with AWS cloud services (S3, Glue, EMR, Athena, Redshift, etc.).

- Database &
- Querying:

Strong command over SQL programming, performance tuning, and database design principles.

- Big Data Frameworks: Familiarity with distributed computing principles and the Apache Spark ecosystem.

- Tools &

- Version Control:

Experience with orchestration tools (e.g., Apache Airflow), containerization, and version control systems (Git).

- Education: Bachelors or Master’s degree in Computer Science, Information Technology, Engineering, or a related quantitative field.

Preferred / Good-to-Have Skills

- Exposure to modern data lakehouse platforms like Databricks or Snowflake.

- Experience working in fast-paced FinTech or Banking domains.

- Familiarity with CI/CD deployment models and infrastructure-as-code concepts.

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