Full-Time

Lead Software Engineer

Data Engineer

Posted on 5/12/2026

Deadline 6/22/26
JP Morgan Chase

JP Morgan Chase

10,001+ employees

Global financial services with diversified offerings

No salary listed

Bengaluru, Karnataka, India

In Person

Category
Data & Analytics (1)
Required Skills
LLM
Data Lake
Python
Apache Spark
Java
ETL
RAG
Data Governance
Snowflake
Requirements
  • Formal training or certification on software engineering concepts and 5+ years of software engineering experience with at least several years in data engineering / big data platforms.
  • Strong hands-on development skills in Python and/or Java (ideally both).
  • Strong knowledge and hands-on experience with GenAI solution development.
  • Strong knowledge of RAG architectures including ingestion, chunking, embeddings, retrieval, grounding, and evaluation.
  • Strong knowledge of Knowledge Graph modeling concepts and practical integration into retrieval and analytics workflows.
  • Strong knowledge of Agentic AI patterns including tool use, workflow orchestration, guardrails, and quality monitoring.
  • Strong experience with Apache Spark and distributed data processing concepts.
  • Proven expertise building ETL/ELT pipelines and data integration frameworks.
  • Strong understanding of data storage/serialization and table/file formats, including Parquet and Avro.
  • Deep understanding of Big Data ecosystem fundamentals (distributed compute, fault tolerance, partitioning, data quality, metadata management).
  • Strong experience implementing Medallion architecture and Data Lake design principles.
  • Strong working knowledge of Snowflake including loading/unloading patterns and performance considerations.
  • Ability to lead technical decisions, drive alignment across teams, and communicate clearly with technical and non-technical stakeholders.
Responsibilities
  • Lead architecture and engineering of large-scale data processing and platform solutions using Python, and Java.
  • Build GenAI solutions using Retrieval-Augmented Generation (RAG) to provide grounded, explainable outputs over enterprise data.
  • Design and integrate Knowledge Graphs to improve entity resolution, contextual retrieval, and relationship-based reasoning
  • Develop Agentic AI workflows to automate tasks using tool orchestration, planning patterns, guardrails, and monitoring.
  • Design and implement robust ETL/ELT pipelines, including ingestion, transformation, validation, reconciliation, and publishing across curated layers.
  • Build and operationalize Medallion architecture patterns for data quality, lineage, governance, and reuse.
  • Develop and optimize solutions on Data Lakes partitioning strategies.
  • Ensure engineering best practices: code quality, testing, CI/CD, observability, security-by-design, and operational readiness.
  • Drive performance optimization across Spark jobs (shuffle tuning, joins, caching, skew handling), storage layout, and Snowflake workloads.
  • Partner with product owners, architects, data governance, and downstream consumers to translate requirements into resilient technical solutions.
  • Mentor and guide engineers; set standards for design reviews, code reviews, incident RCA, and ongoing platform modernization.
Desired Qualifications
  • Experience with data orchestration frameworks and pipeline automation.
  • Experience with data governance concepts (lineage, cataloging, access controls, PII handling) and production operations.
  • Exposure to streaming/event-driven patterns and incremental processing strategies.
  • Experience designing reusable data products, frameworks, or platform components used by multiple teams.
  • Domain experience in highly regulated environments (risk, audit, compliance, privacy).
  • Experience with lakehouse patterns, table formats (e.g., ACID table layers), and data platform modernization programs.
  • Experience with cost optimization and FinOps-style controls for big data workloads.

A global financial services firm offering investment banking, asset management, private equity, financial services, and consumer banking to individuals and institutions. It works by providing advisory, lending, trading, and financing services through a worldwide network, earning revenue from interest, fees, and trading commissions, and using its data and the JPMorgan Chase Institute to analyze economies. It stands apart from peers due to its size, full-range services across consumer and corporate markets, extensive market access, and in-house data-driven insights. Its goal is to deliver comprehensive financial products with integrity and growth while supporting clients and communities through data-backed analysis and targeted programs.

Company Size

10,001+

Company Stage

IPO

Headquarters

New York City, New York

Founded

1959

Simplify Jobs

Simplify's Take

What believers are saying

  • Institutional clients migrate treasury activity to JPMorgan's blockchain infrastructure, generating recurring fees.
  • AI investment of $2B annually drives operational efficiency without workforce reduction, improving margins.
  • 23% return on tangible common equity with 71M digital customers supports sustained profitability growth.

What critics are saying

  • Starmer's ouster triggers £9.9B London Canary Wharf project cancellation if successor government hostile.
  • SEC reverses yield-bearing tokenized asset rules, forcing Kinexys platform redemption and client litigation.
  • Branch expansion into low-income markets increases credit losses and fraud exposure in underserved segments.

What makes JP Morgan Chase unique

  • Tokenized fund leadership on Ethereum with $32B RWA market capturing institutional treasury migration.
  • Coordinated fraud prevention ecosystem through $14M philanthropic investment complementing internal AI defenses.
  • Geographic branch expansion into 500 locations by 2027 targeting underserved rural communities.

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Benefits

Health Insurance

Flexible Work Hours

Paid Sick Leave

Paid Holidays

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