943 Data Analyst Python Hadoop Hive Hql jobs in New York,NY

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(BCBA) Behavior Analyst

Spectrum 360 A NJ Non Profit

Verona, NJ 07044

~ 38 min OnsiteUrgently HiringEducation AssistanceHealth InsurancePaid Time OffRetirement Benefit

Must hold a Master's degree in Applied Behavior Analysis, Education, Psychology or related field and have current certification as a BCBA
Teacher of Students with Disabilities, CE, CEAS preferred
Experience with conducting Functional Behavior Assessments (FBA) and developing Behavior Intervention Plans and Positive Behavior Supports
Proficient in Behavior Skills Training
Demonstrates ability to analyze data and make data based decisions
Effective and professional communication and interpersonal skills
Required criminal history background checks and proof of US citizenship or legal resident alien status

SmartExplore AI is experimental.

New, Posted 18 hours ago

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Sr. Data Engineer

Subway Restaurants

Wilton, CT 06897

$119,200-$149,000/yr

~ 1 hr 8 min OnsiteUrgently HiringEducation AssistanceHealth InsuranceRetirement Benefit

Bachelor's degree in Computer or Information Science or related field, or equivalent combination of education and experience
5–8 years creating quality data pipelines and system integrations
At least 3 years of experience in a cloud environment
Hands-on experience building lakehouse solutions on Databricks (Delta Lake, Unity Catalog) or Snowflake (Iceberg Tables, Horizon Catalog)
Proven ability to design Bronze/Silver/Gold layers for curated, analytics-ready data
Experience implementing Lambda or Kappa architectures using Databricks Structured Streaming / DLT or Snowflake Dynamic Tables / Snowpipe Streaming
Experience building and managing semantic models using Databricks AI/BI Genie or Unity Catalog Metrics or Snowflake Semantic Views / Cortex Analyst
Building pipelines with Airflow, Databricks Lakeflow, or Snowflake Openflow; familiarity with dbt is a plus
Strong PySpark or advanced SQL skills; Python for data engineering or automation
Dimensional modeling, Data Vault, or schema design for analytical workloads
Cluster/warehouse sizing, partitioning, clustering keys, Z-ordering, or query optimization
Working knowledge of Unity Catalog (Databricks) or Horizon Catalog (Snowflake) for lineage, access control, or data quality
Git, Databricks Asset Bundles or Snowflake CLI/Schemachange, automated testing, or deployment pipelines
AWS (S3, Glue, Kinesis), Azure, or GCP services supporting modern data platforms
Familiarity with Databricks Mosaic AI or Snowflake Cortex for GenAI or ML use cases
Strong communication skills to partner with Product Owners, Analysts, or cross-functional engineering teams
Authorized to work in the country the position is based

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Principal Engineer - Python API Development

Fidelity Investments

Harrison, NJ 07029

~ 23 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.

New, Posted 18 hours ago

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Sr. Data Engineer

Subway Restaurants

Westport, CT 06880

$119,200-$149,000/yr

~ 1 hr 6 min OnsiteUrgently HiringEducation AssistanceHealth InsuranceRetirement Benefit

Bachelor's degree in Computer or Information Science or related field, or equivalent combination of education and experience
5–8 years creating quality data pipelines and system integrations
At least 3 years of experience in a cloud environment
Hands-on experience building lakehouse solutions on Databricks (Delta Lake, Unity Catalog) or Snowflake (Iceberg Tables, Horizon Catalog)
Proven ability to design Bronze/Silver/Gold layers for curated, analytics-ready data
Experience implementing Lambda or Kappa architectures using Databricks Structured Streaming / DLT or Snowflake Dynamic Tables / Snowpipe Streaming
Experience building and managing semantic models using Databricks AI/BI Genie or Unity Catalog Metrics or Snowflake Semantic Views / Cortex Analyst
Building pipelines with Airflow, Databricks Lakeflow, or Snowflake Openflow; familiarity with dbt is a plus
Strong PySpark or advanced SQL skills; Python for data engineering or automation
Dimensional modeling, Data Vault, or schema design for analytical workloads
Cluster/warehouse sizing, partitioning, clustering keys, Z-ordering, or query optimization
Working knowledge of Unity Catalog (Databricks) or Horizon Catalog (Snowflake) for lineage, access control, or data quality
Git, Databricks Asset Bundles or Snowflake CLI/Schemachange, automated testing, or deployment pipelines
AWS (S3, Glue, Kinesis), Azure, or GCP services supporting modern data platforms
Familiarity with Databricks Mosaic AI or Snowflake Cortex for GenAI or ML use cases
Strong communication skills to partner with Product Owners, Analysts, or cross-functional engineering teams
Authorized to work in the country the position is based

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.

Recommended

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Compensation & Retirement Analyst

MJHS

New York, NY 10040

Onsite

Bachelor's degree in Human Resources, Business Administration, Finance, or a related field.
Minimum of 3–5 years of experience in compensation required.
Working knowledge of compensation principles, retirement plans, and related regulatory requirements.
Experience supporting merit, bonus, and pay administration cycles.
Proficiency with HRIS systems, payroll platforms, and Microsoft Office applications.

SmartExplore AI is experimental.

New, Posted 9 hours ago

ResumeLibrary

Senior AWS Redshift Data Engineer (Contract | 1099)

BrainTrust

New York, NY 10001

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Quality Manager - Manufacturing

Versa Products Company

Paramus, NJ 07652

~ 29 min OnsiteEducation AssistanceHealth InsurancePaid Time OffRetirement Benefit

Bachelor's Degree in Engineering, Business Administration, or related field preferred
Minimum of 5 years of experience in quality management within a manufacturing environment
Minimum of 5 years of leadership experience managing teams
Strong knowledge of:
Quality Management Systems (QMS)
ISO 9001 standards
ERP systems
Mechanical component inspection
Continuous improvement methodologies (Lean, Six Sigma)
Lean Six Sigma Green Belt or Black Belt strongly preferred
Hands-on experience with precision inspection tools (calipers, CMM, Keyence, etc.)
Ability to define problems, collect data, and draw valid conclusions.
Ability to interpret technical instructions, drawings, and specifications.
Strong attention to detail with a bias for action and results.
Ability to work effectively in a dynamic manufacturing environment.
Adaptability in situations involving change, ambiguity, and competing priorities.