AWS Data Engineer
Job Description
Job Title: Data Engineer – PySpark, Databricks, AWS
We are looking for a skilled Data Engineer with hands-on experience in PySpark, Databricks, Python, SQL, and AWS to build and optimize scalable data solutions. The ideal candidate should have experience designing and developing ETL pipelines, processing large datasets, and implementing data engineering best practices in a cloud environment.
Key responsibilities include developing data pipelines using PySpark and Databricks, writing efficient SQL queries, building reusable data transformation frameworks using Python, and leveraging AWS services such as S3, Glue, EMR, or Lambda for data processing. The candidate should be familiar with Delta Lake, performance optimization, data quality, and workflow orchestration. Experience with CI/CD, Git, Agile methodologies, and data warehouse concepts is preferred.
The ideal candidate should possess strong analytical and problem-solving skills, excellent communication abilities, and the capability to work in a collaborative, fast-paced environment. Knowledge of Spark optimization, data modeling, and cloud-native data architectures will be an added advantage.
Required Skills: PySpark, Databricks, Python, SQL, AWS
Experience: 3–8 years (or as per requirement)
Location: Open
Notice Period: Immediate to 30 days preferred
Skills: python,pyspark,sql,data,aws
We are looking for a skilled Data Engineer with hands-on experience in PySpark, Databricks, Python, SQL, and AWS to build and optimize scalable data solutions. The ideal candidate should have experience designing and developing ETL pipelines, processing large datasets, and implementing data engineering best practices in a cloud environment.
Key responsibilities include developing data pipelines using PySpark and Databricks, writing efficient SQL queries, building reusable data transformation frameworks using Python, and leveraging AWS services such as S3, Glue, EMR, or Lambda for data processing. The candidate should be familiar with Delta Lake, performance optimization, data quality, and workflow orchestration. Experience with CI/CD, Git, Agile methodologies, and data warehouse concepts is preferred.
The ideal candidate should possess strong analytical and problem-solving skills, excellent communication abilities, and the capability to work in a collaborative, fast-paced environment. Knowledge of Spark optimization, data modeling, and cloud-native data architectures will be an added advantage.
Required Skills: PySpark, Databricks, Python, SQL, AWS
Experience: 3–8 years (or as per requirement)
Location: Open
Notice Period: Immediate to 30 days preferred
Skills: python,pyspark,sql,data,aws
More Info
Key Skills
About Company
Skills:
Sql, Aws S3, Lambda, Api Gateway, Kafka, Linux, Python, Redshift, Crawler, AWS Cloud platform, Athena, Dynamic DB, Glue
Skills:
AWS, Sql, Databricks, Python, Git, Data Modelling, Pyspark, Airflow, AWS Step Functions
Skills:
AWS, AWS Glue, Apache, Emr, iceberg
Skills:
Pyspark, Python, Glue, SCD Type 2
Skills:
Kinesis, AWS Glue, Dynamodb, Aws Redshift, Aurora, Glue Catalog, AWS EMR, AWS DMS