Avp Data Engineer

New York,NY

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691 Avp Data Engineer jobs in New York,NY

New, Posted 1 day ago

Senior Vice President, Forward Deployed Engineer

BNY

New York, NY 10007

$104,000-$210,000/yr

Vice President of Quality, Behavioral Health

Northwell Health

Queens, NY 11368

New, Posted 12 hours ago

Vice President, Product Design

BNY

New York, NY 10007

New, Posted 1 day ago

USA SCIB Portfolio Manager, VP - CIB Lending

Santander Holdings USA

New York, NY 10012

The SCIB Portfolio Manager is a Vice President role within US SCIB COO Lending Solutions responsible for portfolio management, credit monitoring and credit administration activities across SCIB lending portfolios.
Completion of bank-certified course in Credit Training - Required.
Applicants must be legally authorized to work in the United States on a full-time basis without requiring employer sponsorship to commence employment.

SmartExplore AI is experimental.

New, Posted 1 day ago

Actuary&AVP - Hybrid

DW Simpson

New York, NY 10038

Vice President and System Chief, Pediatric Cardiology

Northwell Health

New York, NY 10025

New, Posted 17 hours ago

USA SCIB Portfolio Manager, VP - CIB Lending

Santander Holdings USA

New York, NY 10012

New, Posted 21 hours ago

Lead Software Engineer - Data Streaming/Integration Platforms

Disney Entertainment and ESPN Product & Technology Careers

New York, NY 10025

$155,700-$208,700/yr

DIRECT HIRE VP of Operations Assignment in New Hyde Park, New York with Bonus Potential

Clinical Magnet

New Hyde Park, NY 11042

Principal Engineer - Python API Development

Fidelity Investments

Secaucus, NJ 07094

~ 18 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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