AWS Data Engineer
About the role
- Develop and Maintain Data Integration Solutions: Design and implement data integration workflows using AWS Glue/EMR, AWS MWAA(Airflow), Lambda, Redshift
- Demonstrate proficiency in Pyspark, Apache Spark and Python for data processing large datasets
- Ensure data is accurately and efficiently extracted, transformed, and loaded into target systems. Ensure Data Quality and Integrity: Validate and cleanse data to maintain high data quality.
- Ensure data quality and integrity by implementing monitoring, validation, and error handling mechanisms within data pipelines
- Optimize Data Integration Processes: Enhance the performance, optimization of data workflows to meet SLAs, scalability of data integration processes and cost-efficiency on AWS cloud infrastructure.
- Identify and resolve performance bottlenecks, fine-tuning queries, and optimizing data processing to enhance Redshift's performance
- Regularly review and refine integration processes to improve efficiency.
- Support Business Intelligence and Analytics: Translate business requirements to technical specifications and coded data pipelines
- Ensure timely availability of integrated data for business intelligence and analytics.
Before you apply
Applying takes about a minute. These four things decide how fast it moves after that.
Your profile is current
It's what we read first. Occupations, seniority and locations matter more than a long history.
Two examples you can talk through
Not a portfolio — just two pieces of work where you can explain the decisions and what you'd change.
A number in mind
What you're on now and what would make you move. We negotiate better when we know both.
Your notice period
Employers plan around it, and it's the question that stalls offers most often.
Once you apply, someone reads it and calls you before anything reaches the employer — usually within two working days.
