
Senior Data Engineer
2 days ago
This role is for one of the Weekday's clients
Min 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
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