800 Data Engineer Human Resources jobs in New York,NY

Degree in Chemistry, Engineering, or related technical discipline.
Up to 25% travel should be expected with this role
This may include pre-placement drug screening, medical review of physical fitness for the role, and background checks.

SmartExplore AI is experimental.

New, Posted 6 hours ago

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Head of Technology, Intelligence Ventures

Spectrum

New York, NY 10036

Onsite

Deep expertise across the full intelligence platform stack — including distributed data pipelines, ML platform architecture, embedding systems, feature stores, agent-to-agent API design, and LLM-powered application layers — with demonstrated ability to architect and ship all layers as a coherent, production-grade product
Demonstrated experience architecting and operating consumer data intelligence or data product platforms underpinned by complex machine learning systems and built on modern, cloud-native data infrastructure
Hands-on proficiency with Snowflake (including Cortex, Native Apps, and data sharing frameworks), cloud data platforms (AWS, Azure, or GCP), and production ML/AI systems at scale
Experience building agentic AI systems and LLM-powered product interfaces — including agent-to-agent APIs, retrieval-augmented generation architectures, and natural language UIs grounded in proprietary data — with strong product instincts around accuracy, trust, and user experience for non-technical enterprise audiences
Proven ability to translate complex technical architecture into clear executive and partner-facing communications; comfortable engaging at the C-suite level and in strategic partner negotiations with hyperscalers and technology platforms
Strong understanding of privacy-preserving data architecture, including differential privacy, de-identification techniques, zero-copy and clean room frameworks, and the regulatory landscape governing consumer behavioral data
Track record of recruiting and developing exceptional engineering talent in competitive markets; experience building high-performance teams from early-stage through scaled operations
Experience managing external development partners and outsourced engineering resources alongside an internal team in a fast-moving, build-from-scratch environment
Ability to operate in an ambiguous, early-stage environment while maintaining rigorous engineering standards and delivering against aggressive milestones
Experience building externally-consumed data or intelligence platforms — including developer-facing APIs, data marketplace integrations, or partner-accessible data products — with strong instincts around developer experience, access governance, and commercial platform design
Bachelor's Degree in Computer Science, Software Engineering, Electrical Engineering, or a related technical field
Experience leading consumer data intelligence, AI/ML platform, or data product engineering organizations — 10 years
Engineering leadership and team-building experience, including managing senior technical staff — 7 years

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Principal Engineer - Python API Development

Fidelity Investments

Jersey City, NJ 07311

~ 22 min OnsiteEducation AssistanceHealth InsurancePaid Time OffRetirement Benefit

Bachelor's or Master's degree in Computer Science, Software Engineering, or a closely related engineering discipline
8+ years (typically 10+) building and operating production platforms and services at scale
Deep software engineering expertise in Python and distributed systems
A track record of building production‑grade services, libraries, and internal platforms
Linux fluency and scripting are required
Cloud platform leadership (AWS) —hands-on with S3, Lambda, Batch, Step Functions, EventBridge, CloudWatch, and SNS/SQS—and experience shaping platform patterns that other teams adopt
Experience enabling managed ML services (e.g., SageMaker) as part of broader platform capabilities; exposure to Azure or GCP is beneficial
DevOps and CI/CD at scale, owning standards for automated build/test/deploy (e.g., Jenkins, Git‑based workflows), containerization (Docker), release governance, and multi‑environment promotion for ML‑enabled workloads
Infrastructure as Code (CloudFormation, Terraform/OpenTofu) and platform reliability engineering (SLOs/error budgets, capacity planning, cost observability, incident response, and post‑mortems) for ML serving and data/feature pipelines
ML enablement in production: model packaging, deployment strategies (batch/online/streaming), inference routing, traffic management, performance tuning, observability, and controls for responsible use—without a research or modeling focus
Cross‑org technical leadership: you mentor junior and senior engineers, are a backbone of code review across repos, and routinely consider impacts on upstream/downstream systems when proposing changes
Set platform strategy and standards for ML packaging, deployment, serving, and observability—driving consistent adoption across squads and business units
Partner with Data Scientists to package, scale, and operationalize models; define the APIs, guardrails, and automation that take work from experimentation to reliable production
Enable secure, scalable access to traditional and generative models by collaborating with platform and application engineers to integrate through enterprise gateways and services
Advance model/data observability—tooling for data and feature drift detection, prediction‑quality monitoring and uncertainty signals, and automated diagnostics/ explainability
Lead cross‑platform incident response and post‑mortems, drive systemic fixes, and evolve standards to prevent recurrence—across applications and the platform
Uplevel engineering velocity by introducing reusable frameworks, paved paths, and CI/CD templates that simplify integration, reduce toil, and improve reliability at scale
Reduce cost and complexity across the ML ecosystem through pragmatic technology choices, clear abstractions, and a long‑term platform roadmap
The base salary range for this position is $107,000-216,000 USD per year.
Base salary is only part of the total compensation package. Depending on the position and eligibility requirements, the offer package may also include bonus or other variable compensation.

SmartExplore AI is experimental.

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Director of Engineering

Marine Electric Systems

South Hackensack, NJ 07606

~ 26 min OnsiteHealth InsurancePaid Time OffRetirement Benefit

Bachelor's degree in Electrical Engineering or a closely related engineering discipline.
Significant experience in electrical engineering, sustaining engineering, production engineering, test engineering, or engineering leadership in a manufacturing environment.
Strong hands-on technical knowledge of electrical and electromechanical systems.
Experience with PCB troubleshooting, electrical testing, components, assemblies, and production support.
Ability to read and interpret drawings, schematics, specifications, technical data packages, and test procedures.
Experience supporting manufacturing, production, repair, or sustainment work.
Ability to make practical engineering decisions with imperfect information.
Strong communication skills and ability to work cross-functionally with production, quality, procurement, sales, and leadership.
Demonstrated ability to mentor junior engineers or technical staff.
Strong organizational skills and ability to create structure in a fast-moving small-company environment.

SmartExplore AI is experimental.

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Principal Engineer - Python API Development

Fidelity Investments

Hoboken, NJ 07030

~ 16 min OnsiteEducation AssistanceHealth InsurancePaid Time OffRetirement Benefit

Bachelor's or Master's degree in Computer Science, Software Engineering, or a closely related engineering discipline
8+ years (typically 10+) building and operating production platforms and services at scale
Deep software engineering expertise in Python and distributed systems
A track record of building production‑grade services, libraries, and internal platforms
Linux fluency and scripting are required
Cloud platform leadership (AWS) —hands-on with S3, Lambda, Batch, Step Functions, EventBridge, CloudWatch, and SNS/SQS—and experience shaping platform patterns that other teams adopt
Experience enabling managed ML services (e.g., SageMaker) as part of broader platform capabilities; exposure to Azure or GCP is beneficial
DevOps and CI/CD at scale, owning standards for automated build/test/deploy (e.g., Jenkins, Git‑based workflows), containerization (Docker), release governance, and multi‑environment promotion for ML‑enabled workloads
Infrastructure as Code (CloudFormation, Terraform/OpenTofu) and platform reliability engineering (SLOs/error budgets, capacity planning, cost observability, incident response, and post‑mortems) for ML serving and data/feature pipelines
ML enablement in production: model packaging, deployment strategies (batch/online/streaming), inference routing, traffic management, performance tuning, observability, and controls for responsible use—without a research or modeling focus
Cross‑org technical leadership: you mentor junior and senior engineers, are a backbone of code review across repos, and routinely consider impacts on upstream/downstream systems when proposing changes
Set platform strategy and standards for ML packaging, deployment, serving, and observability—driving consistent adoption across squads and business units
Partner with Data Scientists to package, scale, and operationalize models; define the APIs, guardrails, and automation that take work from experimentation to reliable production
Enable secure, scalable access to traditional and generative models by collaborating with platform and application engineers to integrate through enterprise gateways and services
Advance model/data observability—tooling for data and feature drift detection, prediction‑quality monitoring and uncertainty signals, and automated diagnostics/ explainability
Lead cross‑platform incident response and post‑mortems, drive systemic fixes, and evolve standards to prevent recurrence—across applications and the platform
Uplevel engineering velocity by introducing reusable frameworks, paved paths, and CI/CD templates that simplify integration, reduce toil, and improve reliability at scale
Reduce cost and complexity across the ML ecosystem through pragmatic technology choices, clear abstractions, and a long‑term platform roadmap
The base salary range for this position is $107,000-216,000 USD per year.
Base salary is only part of the total compensation package. Depending on the position and eligibility requirements, the offer package may also include bonus or other variable compensation.

SmartExplore AI is experimental.

Apply Directly

Associate or Senior Associate, Fixed Assets / Cost Segregation Engineer (AMCS)

KPMG

New York, NY 10025

OnsiteHealth InsuranceRetirement Benefit

Bachelor's degree from an accredited college/university in engineering, architecture, construction management, or a closely related discipline is required
Minimumone year of recent experience in an engineering firm, corporation, or public accounting firm; background in cost segregation, fixed asset consulting, construction, engineering, or a related technical field is preferred
Understanding of construction processes and building systems, and how project costs translate into asset classifications
Basic knowledge of federal income tax depreciation principles and accounting for capital expenditures, or a demonstrated interest in developing this skillset
Experience reviewing technical drawings, construction documentation, or large project cost datasets
Strong proficiency in Excel (such as advanced formulas, pivot tables, lookups) and experience working with structured data from financial or operational systems; demonstrated experience or strong aptitude with data-wrangling/automation tools (such as Power Query, Alteryx), business intelligence tools (such as Power BI, Tableau), or query/scripting languages (such as SQL, Python, R)
Applicant must be authorized to work in the U.S. without the need for employment-based visa sponsorship now or in the future. KPMG LLP will not sponsor applicants for U.S. work visa status for this opportunity (no sponsorship is available for H-1B, L-1, TN, O-1, E-3, H-1B1, F-1, J-1, OPT, CPT or any other employment-based visa)

SmartExplore AI is experimental.

New, Posted 23 hours ago

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