Lead Data Engineer
2 weeks ago
Key Responsibilities:
- Design, develop, and maintain scalable and secure ETL pipelines using PySpark, Python, and SQL.
- Build and orchestrate data workflows leveraging AWS services such as Glue, Lambda, Step Functions, S3, Athena, and Redshift.
- Perform data profiling, cleansing, and transformation to ensure data quality and consistency.
- Optimize and tune data pipelines for performance and cost-efficiency.
- Work with data architects, analysts, and stakeholders to understand data requirements and translate them into technical solutions.
- Develop and maintain technical documentation for all ETL processes and data flows.
- Monitor data pipelines and troubleshoot any issues related to performance, failures, or data inconsistencies.
- Participate in Agile/Scrum development cycles and contribute to sprint planning and retrospectives.
Required Skills & Qualifications:
- 7+ years of hands-on experience in ETL development and data engineering.
- Strong programming skills in Python and PySpark.
- Expert-level knowledge of SQL for complex data manipulation and analysis.
Experience with AWS services, including but not limited to:
AWS Glue
- AWS Lambda
- Amazon S3
- Amazon Redshift
- AWS Step Functions
Amazon Athena
Solid understanding of data warehousing concepts, data modeling, and schema design.
- Experience working with large-scale distributed data systems and real-time/batch processing.
- Familiarity with version control tools (e.g., Git) and CI/CD pipelines.
- Excellent problem-solving and communication skills
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