Job description

Position Overview We are seeking a Junior AWS Databricks Data Engineer to build and maintain scalable data pipelines on AWS. This role will turn data from databases, APIs, and files into reliable datasets for analytics and business reporting. Responsibilities: • Develop batch and incremental ETL/ELT pipelines using Databricks, PySpark, Spark SQL, and Delta Lake. • Build and maintain Bronze, Silver, and Gold data layers using Medallion Architecture. • Ingest and transform structured and semi-structured data from sources such as Oracle, SQL Server, REST APIs, and Amazon S3. • Implement data quality checks, including schema validation, deduplication, reconciliation, and business rule enforcement. • Optimize Spark jobs and Delta tables to improve processing time and control cloud resource use. • Publish curated datasets to Amazon Redshift and support reporting in Power BI, Tableau, or Amazon QuickSight. • Orchestrate, monitor, and troubleshoot production pipelines using Databricks Workflows, Apache Airflow, and AWS monitoring services. • Collaborate with analysts, architects, QA, and DevOps teams on requirements, releases, and production support. Required Qualifications: • 5+ years of overall technology experience. • 3+ years of AWS engineering experience. • Strong Python and SQL skills, with hands-on experience in PySpark, Databricks, and Delta Lake. • Experience building data pipelines on AWS, particularly with Amazon S3, AWS Glue, and Amazon Redshift. • Knowledge of incremental loading, dimensional data modeling, data quality practices, and Spark performance tuning. • Experience with Git, CI/CD workflows, and Agile delivery. Preferred Qualifications : • Experience with Databricks Auto Loader, Unity Catalog, and AWS Glue Data Catalog. • Experience supporting manufacturing, supply chain, quality, or operational analytics. • Databricks Certified Data Engineer Associate and AWS Certified Data Engineer Associate certification.

Requirements

Must-haves

Position Overview
Develop batch and incremental ETL/ELT pipelines using Databricks, PySpark, Spark SQL, and Delta Lake.
Build and maintain Bronze, Silver, and Gold data layers using Medallion Architecture.
Ingest and transform structured and semi-structured data from sources such as Oracle, SQL Server, REST APIs, and Amazon S3.
Implement data quality checks, including schema validation, deduplication, reconciliation, and business rule enforcement.
Optimize Spark jobs and Delta tables to improve processing time and control cloud resource use.
Publish curated datasets to Amazon Redshift and support reporting in Power BI, Tableau, or Amazon QuickSight.
Orchestrate, monitor, and troubleshoot production pipelines using Databricks Workflows, Apache Airflow, and AWS monitoring services.
Collaborate with analysts, architects, QA, and DevOps teams on requirements, releases, and production support.
5+ years of overall technology experience.
3+ years of AWS engineering experience.
Strong Python and SQL skills, with hands-on experience in PySpark, Databricks, and Delta Lake.
Experience building data pipelines on AWS, particularly with Amazon S3, AWS Glue, and Amazon Redshift.
Knowledge of incremental loading, dimensional data modeling, data quality practices, and Spark performance tuning.
Experience with Git, CI/CD workflows, and Agile delivery.

Nice-to-haves

Experience with Databricks Auto Loader, Unity Catalog, and AWS Glue Data Catalog.
Experience supporting manufacturing, supply chain, quality, or operational analytics.
Databricks Certified Data Engineer Associate and AWS Certified Data Engineer Associate certification.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

awssqlrestpythonci/cdagile

Platform: oracle-cloud
Effort: medium