Enigma is an analytics-focused application that transforms ServiceNow-sourced ITSM data in Snowflake into curated datasets and delivers reporting, search, and insights. We’re expanding APIs for broader firm consumption and planning a revised data architecture—while preserving the fast search experience users value today.
We’re hiring a mid-level Data Engineer to build curated analytical datasets and implement high-performance “data serving” patterns for search and APIs. This role will also contribute to evaluating architecture options (e.g., Snowflake-only vs. a dedicated serving/search layer) based on latency, cost, and operational fit.
Job Responsibilities
- Build and operate ELT pipelines into Snowflake from ServiceNow-derived sources, including incremental loads, backfills, and reprocessing.
- Develop and maintain analytics models and canonical definitions (metrics, dimensions, incident taxonomy, time-windowed aggregates).
- Design low-latency data-serving patterns for search and APIs, such as:
- denormalized/search-optimized tables and materialized aggregates
- precomputed facets/filters and pagination-friendly query patterns
- strategies to minimize expensive joins at request time
- Contribute to architecture decisions for search performance:
- define measurable non-functional requirements (e.g., p95/p99 latency targets, concurrency, freshness)
- run/assist PoCs and benchmark approaches (warehouse-only vs. dedicated serving/search layer)
- document tradeoffs across latency, cost, complexity, and operational risk
- Implement data quality and observability: freshness/completeness checks, schema drift detection, reconciliation, monitoring/alerting, and clear SLAs/SLOs.
- Partner with application developers to create stable, versioned data surfaces for APIs (contract-friendly schemas, safe schema evolution).
- Create and maintain runbooks and support processes; participate in incident triage where data quality or serving performance is involved.
- Build and maintain an MCP (Model Context Protocol) server—a standardized adapter that connects AI applications (e.g., LLMs/agents) to external tools, APIs, and data sources by translating AI intent into actionable system commands.
- Uses enterprise-authorized AI capabilities within the work environment to accelerate data pipeline/design analysis and documentation, validating outputs and handling data according to sensitivity and security requirements.
- Applies reuse-first, AI-assisted practices to strengthen SDLC-quality routines for data pipelines (e.g., test generation and control validation), ensuring traceability/auditability and alignment to resiliency and security expectations.
Required qualifications, capabilities, and skills
- 3–6 years (or equivalent) experience delivering production data pipelines and curated analytical datasets.
- Strong SQL and analytics modeling skills (incremental patterns, snapshots, aggregates).
- Proficiency in Python (preferred) and/or Java for transformations, validations, and automation.
- Experience with Snowflake, including performance/cost awareness, query tuning, and secure access patterns.
- Demonstrated ability to design for performance (benchmarking, query plan reasoning, caching/materialization strategies).
- Demonstrated experience using enterprise-authorized AI capabilities within the work environment to support data engineering workflows with strong validation habits and awareness of data sensitivity.
- Ability to review and validate AI-assisted outputs (e.g., query suggestions, test ideas, or model change summaries) before use, escalating when uncertain and following data handling requirements.
- Advanced English skills
Preferred qualifications, capabilities, and skills
- Experience building integrations/servers that connect applications to tools/APIs/data sources (experience with MCP specifically is a plus).
- Experience with low-latency serving/search technologies (e.g., Elasticsearch/OpenSearch, Postgres-based serving layers, etc.).
- Familiarity with ITSM / ServiceNow datasets.
- Experience supporting data products used by multiple downstream teams (documentation, versioning, consumer enablement/change management).
Skills Required
- 3-6 years experience delivering production data pipelines and curated analytical datasets
- Strong SQL and analytics modeling skills (incremental patterns, snapshots, aggregates)
- Proficiency in Python and/or Java for transformations, validations, and automation
- Experience with Snowflake, including performance/cost awareness, query tuning, and secure access patterns
- Demonstrated ability to design for performance (benchmarking, query plan reasoning, caching/materialization strategies)
- Demonstrated experience using enterprise-authorized AI capabilities within the work environment and validating outputs with data sensitivity awareness
- Ability to review and validate AI-assisted outputs and follow data handling requirements
- Advanced English skills
- Experience building integrations/servers that connect applications to tools/APIs/data sources (MCP experience a plus)
- Experience with low-latency serving/search technologies (Elasticsearch/OpenSearch, Postgres-based serving layers)
- Familiarity with ITSM / ServiceNow datasets
- Experience supporting data products used by multiple downstream teams (documentation, versioning, consumer enablement)
JPMorganChase Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about JPMorganChase and has not been reviewed or approved by JPMorganChase.
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Healthcare Strength — Medical, dental, vision, and mental health coverage are comprehensive, with on-site clinics, preventive care, and specialized supports such as maternity nurse guidance and fertility treatments. Wellness activities can help offset copays and out-of-pocket costs, reinforcing the perceived strength of health benefits.
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Retirement Support — A 401(k) with dollar-for-dollar matching and additional automatic pay credits reflect strong employer-backed retirement savings. An employee stock purchase plan and related financial programs further bolster long-term financial support.
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Leave & Time Off Breadth — Paid time off, sick time, holidays, and generous parental leave are provided alongside family medical leave and adoption/fertility assistance. Additional programs like caregiver support and volunteer time off expand the breadth of time-away options.
JPMorganChase Insights
What We Do
JPMorgan Chase & Co. (NYSE: JPM) is a leading global financial services firm with assets of $3.7 trillion and operations worldwide. The firm is a leader in investment banking, financial services for consumers and small businesses, commercial banking, financial transaction processing, and asset management. A component of the Dow Jones Industrial Average, JPMorgan Chase & Co. serves millions of consumers in the United States and many of the world’s most prominent corporate, institutional and government clients under its J.P. Morgan and Chase brands. Technology fuels every aspect of our company and is at the heart of everything we do. With over 50,000 technologists globally and an annual tech spend of $12 billion, we are dedicated to improving the design, analytics, development, coding, testing and application programming that goes into creating high quality software and new products. Learn more about technology at our firm, explore resources from our Distinguished Engineers, AI & ML researchers, and other experts; access the latest episode of our TechTrends podcast, and more at www.jpmorgan.com/technology. Information about JPMorgan Chase & Co. is available at www.jpmorganchase.com. ©2023 JPMorgan Chase & Co. All rights reserved. JPMorgan Chase is an Equal Opportunity Employer, including Disability/Veterans.
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