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SAIC

Junior AWS Databricks Developer

SAIC

. Develop batch and incremental ETL/ELT pipelines using Databricks, PySpark, Spark SQL, and Delta Lake .

Posted 10/16/2026full-timeCollege Park • Maryland • United StatesJuniorWebsite

Core Competencies

Role fit
Core Competencies

Use this summary to align your resume positioning with the role.

Proficient in developing ETL/ELT pipelines using Databricks, PySpark, and Delta Lake, with a strong focus on data quality practices and performance optimization. Experienced in collaborating with cross-functional teams to deliver robust data solutions on AWS, leveraging tools like Amazon Redshift and AWS Glue.

Highest-signal resume keywords
ETL/ELT Pipeline DevelopmentAWS Engineering ExperiencePython ProgrammingData Quality PracticesSpark Performance Tuning

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills
DatabricksPySparkSpark SQLDelta LakeSQLData Pipeline DevelopmentData Quality ChecksIncremental LoadingDimensional Data ModelingSchema Validation
Soft Skills
CollaborationTroubleshootingCommunication
Tools & Technologies
Amazon S3Amazon RedshiftPower BITableauAmazon QuickSightApache AirflowAWS GlueGitCI/CD Workflows
Industry Keywords
Medallion ArchitectureCloud Resource OptimizationAgile DeliveryData Layer Management

Tech Stack

Tools & technologies
AirflowAmazon RedshiftApacheAWSCloudETLOraclePySparkPythonSparkSQLTableau

About the role

Key responsibilities & impact
  • 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 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

Requirements

What you’ll need
  • 5+ years of overall technology experience
  • 3+ years of AWS engineering experience
  • Strong Python and SQL skills
  • Hands-on experience with 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