JPMorgan Chase & Co., Hyderabad, Telangana, India
Help modernize cloud data engineering by delivering trusted lakehouse products and shaping responsible AI-assisted engineering practices that improve engineering throughput and quality.
Job summary
pipelines on AWS using Databricks, PySpark, Spark, Delta Lake, AWS Glue, SQL, and lakehouse patterns for batch and streaming workloads.
reporting, and publishing pipelines that produce maintainable, well-modeled data products.
engineering practices including testing, peer review, performance tuning, resiliency, and secure coding.
including dependency management, retries, backfills, service level agreement monitoring, and operational runbooks.
automated quality gates (linting, unit and integration tests, security scans, data-quality checks, schema validation, and release controls).
CloudFormation, and create architecture, design, lineage, and operational artifacts that meet scalability, performance, cost, resiliency, governance, and security requirements.
(Copilot-assisted development, Claude-enabled refactoring and troubleshooting, and reusable AI skills and task-oriented agents) and integrate approved developer tools with Jira, source control, documentation, observability, incident triage, and release readiness—ensuring controls for human review, secrets protection, and safeguards for personally identifiable information and payment card information.
delivering production data platforms and pipelines.
Lake, and lakehouse architectures on AWS.
to write clean, testable, maintainable, production-grade code.
jobs, cataloging, identity and access management security, monitoring, and operational support.
design, scheduling, backfills, dependency management, monitoring, and recovery.
operationalization, source onboarding, and reliability practices.
infrastructure as code (Terraform and/or AWS CloudFormation), automated testing and quality gates, release controls, agile delivery, DevOps practices, data governance, resiliency engineering, secure-by-design implementation, AI-assisted workflows (including GitHub Copilot and Claude) with human review, reusable AI skills/prompts/agents, and controls for sensitive data (personally identifiable information and payment card information), secrets management, access control, auditability, and secure logging.
deployment validation, data-quality checks, lineage generation, developer enablement, or operational workflows.
documentation platforms, Databricks, observability platforms, or internal developer tooling.
management, and modern analytics and business intelligence ecosystems (for example, ThoughtSpot), including streaming and event-driven ingestion (for example, Kafka) and enterprise observability tooling.
AWS Developer Associate, AWS Data Analytics Specialty, or Databricks Data Engineer certification) and experience mentoring engineers and leading standards for AI-assisted data engineering delivery.
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Source: the company job board. We last checked this job 21 h ago. We first saw it 21 h ago. A job on a job board does not always mean that the team interviews people now.