Senior Data Engineer

This role is for one of the Weekday's clientsMin Experience: 5 years

Location: Gurgaon

JobType: full-time

We are seeking an experienced Senior Data Engineer with strong expertise in building and managing large-scale data pipelines within the AWS ecosystem. The ideal candidate will have a solid background in SQL, cloud-native data platforms, and orchestration frameworks, with a deep understanding of scalable data lake and warehouse architectures.

Requirements

Key Responsibilities

• Design, develop, and maintain robust, scalable data pipelines and ETL/ELT workflows.
• Leverage the AWS Data Stack (S3, Glue, Kinesis, Redshift, Data Lake) for data ingestion, transformation, and storage.
• Write, optimize, and manage complex SQL queries for analytics and reporting needs.
• Implement and manage data orchestration frameworks (e.g., Airflow, Step Functions) to automate and monitor workflows.
• Utilize Databricks for advanced data processing, transformation, and analytics.
• Ensure data quality, integrity, reliability, and security across the pipeline lifecycle.
• Collaborate with data scientists, analysts, and business stakeholders to deliver scalable data-driven solutions.

Required Skills & Qualifications

• 5–7 years of proven experience as a Data Engineer or in a similar role.
• Strong expertise in SQL development and performance optimization.
• Hands-on experience with AWS Data Stack (S3, Glue, Kinesis, Redshift, Data Lake).
• Proficiency in designing and managing orchestration workflows for data pipelines.
• Experience with Databricks for data engineering and transformation.
• Strong understanding of data modeling, warehousing concepts, and ETL/ELT best practices.
• Ability to thrive in a hybrid working model with cross-functional collaboration.

Nice To Have

• Experience with Python or Scala for data engineering tasks.
• Familiarity with CI/CD pipelines and DevOps practices for data workflows.
• Exposure to real-time streaming pipelines and advanced analytics use cases.

Core Skills

PySpark • SQL • Databricks • AWS