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Lead Software Engineer- Data Engineer/Pyspark/Databricks

JPMorgan Chase
Houston, USA
Full-time
Lead
Onsite
Discovered 2 weeks ago
airflowawsazurebedrockdatabricksdynatrace

Full Job Posting

Job Responsibilities:

  • Develops secure, high-quality production code for data-intensive applications and platforms, and reviews and mentors other engineers
  • Creates durable, reusable software frameworks and patterns that are leveraged across teams and functions
  • Drives adoption of advanced technical methods and practices aligned with the latest industry standards and product development methodologies
  • Advises cross-functional teams on technological matters within your domain of expertise
  • Drives team adoption of enterprise-authorized AI-assisted engineering practices within the work environment to improve code quality, delivery speed, and operational outcomes (e.g., AI-assisted code review/refactoring, test strategy acceleration, incident/root-cause analysis support), while establishing consistent validation standards (secure coding, peer review, automated testing) and promoting reuse of effective patterns across the team.
  • Applies knowledge of tools within the Software Development Life Cycle toolchain, including enterprise-authorized AI-assisted development and automation capabilities, to improve the value realized by automation.
  • Applies knowledge of tools within the Software Development Life Cycle toolchain, including enterprise-authorized AI-assisted development and automation capabilities, to improve the value realized by automation at scale.

Required qualifications, capabilities, and skills

  • Hands-on practical experience delivering system design, application development, testing, and operational stability at enterprise scale
  • Expert in one or more programming languages, particularly Python and/or Java
  • Advanced knowledge of software application development and technical processes, with considerable depth in one or more disciplines (e.g., cloud, AI/ML, data engineering)
  • Demonstrated experience leading effective use of approved AI-assisted software development tools (e.g., for coding, code review, test acceleration, troubleshooting) with the ability to set team expectations for validating AI outputs for correctness, performance, and security.
  • Strong understanding of responsible AI use in engineering workflows, including data sensitivity considerations, secure handling of inputs/outputs, and adherence to resiliency and security expectations; experience coaching engineers on safe, compliant adoption within delivery practices
  • Experience in large-scale data processing, microservices, API design, Kafka, Redis, MemCached, observability tools (Dynatrace, Splunk, Grafana), and orchestration frameworks (Airflow, Temporal)
  • Advanced working knowledge of relational and NoSQL databases, vector stores, data lake architectures, and data governance
  • Practical cloud-native experience (AWS, Azure, or GCP)
  • Ability to present and effectively communicate with senior leaders and executives

Preferred qualifications, capabilities, and skills

  • Experience with modern data platforms such as Databricks or Snowflake
  • Deep hands-on experience with Spark/PySpark and other big data processing technologies
  • Expertise in open-source table formats and catalog services such as Apache Iceberg
  • Experience with LLM orchestration frameworks and model serving infrastructure or managed endpoints (AWS Bedrock, Azure OpenAI)

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