Position: Lead Data Engineer (PySpark, AWS Redshift, Glue, EMR)
Experience: 5 to 8+ Years
Office Locations & Work Schedule: Hybrid work model: 1 day work from office and 4 days work-from-home in a week (either LHR, ISB or KHI office)
Job Summary
We are seeking a highly skilled Lead Data Engineer with 5 to 8+ years of experience in designing, developing, and optimizing large-scale data platforms and ETL/ELT pipelines. The ideal candidate will have strong hands-on expertise in PySpark, AWS Glue, Amazon EMR, Amazon Redshift, and SQL-based data warehousing, along with proven experience in performance tuning and data optimization.
Key Responsibilities
- Design, develop, and maintain scalable data pipelines using PySpark, AWS Glue, and Amazon EMR.
- Build and optimize data ingestion, transformation, and processing frameworks for structured and semi-structured data.
- Develop and maintain enterprise data warehouse solutions using Amazon Redshift.
- Write complex SQL queries, stored procedures, and data transformations to support analytics and reporting requirements.
- Implement ETL/ELT processes to move data efficiently across multiple systems and platforms.
- Perform performance tuning and optimization of Spark jobs, ETL pipelines, SQL queries, and Redshift workloads.
- Ensure data quality, integrity, security, and governance across data platforms.
- Troubleshoot production issues and perform root cause analysis for data-related incidents.
- Collaborate with business stakeholders, analysts, architects, and engineering teams to understand data requirements.
- Participate in code reviews, technical design discussions, and best-practice implementation.
- Monitor data pipelines and proactively identify opportunities for performance improvements and automation.
- Create and maintain technical documentation, data models, and operational procedures.
Required Skills & Experience
- 5 to 8+ years of experience in Data Engineering and Data Warehousing.
- Strong hands-on experience with PySpark.
- Extensive experience with AWS Glue.
- Experience building and managing workloads on Amazon EMR.
- Strong expertise in Amazon Redshift.
- Excellent SQL development and query optimization skills.
- Strong understanding of Data Warehousing concepts, dimensional modeling, and ETL/ELT processes.
- Experience in performance tuning of Spark jobs, SQL queries, ETL pipelines, and data warehouse workloads.
- Experience handling large-scale datasets and distributed data processing.
- Strong debugging, troubleshooting, and analytical skills.
Preferred Skills
- Experience with additional AWS services such as S3, IAM, CloudWatch, Lambda, and Step Functions.
- Knowledge of CI/CD pipelines and DevOps practices for data platforms.
- Experience with workflow orchestration tools.
- Familiarity with data governance, security, and compliance practices.
- Exposure to Agile/Scrum development methodologies.
Qualifications
- Bachelor's degree in Computer Science, Software Engineering, Information Technology, or a related field.
- Relevant AWS certifications will be considered an advantage.