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Software Engineer III - AWS, Databricks, Python, Cognos

JPMorgan Chase
Ohio, USA
Full-time
Mid · 3+ years experience
Onsite
Discovered 2 weeks ago
airflowawsdatabricksfastapigitjenkins

Full Job Posting

Job responsibilities

  • Design and build end-to-end ETL/ELT pipelines from scratch on Databricks using PySpark and SQL, including ingestion, transformation, and publishing of curated datasets for downstream analytics and applications.
  • Develop and orchestrate AWS-based data workflows using Lambda, Step Functions, SQS/SNS, API Gateway, Secrets Manager, and storage services, with robust error handling, retries, and monitoring.
  • Implement orchestration/scheduling and operational support using Apache Airflow / Apache NiFi / Autosys, including SLAs, backfills, runbooks, and production incident triage/root-cause analysis.
  • Build secure, repeatable platform and data-access controls via Terraform and least-privilege principles; partner on access reviews/audit evidence and ensure backup, recovery, and archiving strategies are implemented and tested

Required qualifications, capabilities, and skills

  • Formal training or certification on software engineering concepts and 3+ years applied experience
  • 3+ years of experience using technologies such as Databricks, Pyspark, AWS, is essential and creating ETL Pipeline from scratch is a must.
  • 3+ years of experience working with AWS (Lambda, Step Function, SQS, SNS, API Gateway, secrets manager and storage services) is a must.
  • 3+ years of experience in software engineering and object-oriented programming skills with expertise in Python and Terraform
  • Hands on experience with open-source frameworks/libraries, such as Apache NiFi, Apache Airflow and Autosys.
  • Strong understanding of REST API development using FASTAPI or equivalent frameworks.
  • Advanced at SQL (e.g., joins and aggregations)

Preferred qualifications, capabilities, and skills

  • Familiar with development tools such as Jenkins, Jira, Git/Stash, spinnaker
  • Familiarity with unit testing frameworks such as pytest or unittest.
  • Extensive experience in statistical data analysis, with the ability to select appropriate tools and identify data patterns for effective analysis, as well as experience throughout the data lifecycle

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