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About This Role
1–3 Years Experience Required
Full\-Time Onsite, Midtown East, NY
ABOUT THE ROLE
Interval Partners is a multi\-billion\-dollar alternative investment firm located in Midtown Manhattan. We are seeking a Data Scientist/Engineer to join our team. This role will report to the firm's Data Engineer/Developer and President. This is a hands\-on, ownership\-oriented role focused first on data engineering: building reliable pipelines, maintaining curated datasets, and making alternative data ready for analyst and portfolio manager use. The role also requires strong judgment about what each dataset measures, where its limitations are, and how LLMs, AI agents, and tool\-based workflows can make analysts faster. You will work directly with portfolio managers and senior analysts on questions tied to live investment decisions, with reliable data and targeted AI solutions at the center of the work.
KEY RESPONSIBILITIES
Alternative Data Acquisition, Ingestion \& Pipeline Maintenance
- Build, maintain, and improve Python\-based pipelines for ingesting, processing, and visualizing structured datasets, including alternative data such as credit/debit card transactions, point\-of\-sale data, and internal data assets.
- Collaborate on ingestion workflows that are reliable, repeatable, observable, and easy to maintain, with clear treatment of schema changes, late\-arriving data, vendor restatements, duplicate records, and missing values.
- Develop automated checks for data completeness, consistency, timeliness, and accuracy, and create clear escalation paths when pipeline failures or data quality issues occur.
Data Cleaning, Transformation \& Readiness
- Transform raw data into clean, well\-structured, analysis\-ready outputs that map to relevant business metrics, key performance indicators, and analyst research workflows.
- Perform data profiling, normalization, enrichment, deduplication, entity resolution, outlier handling, and quality remediation to improve downstream usability.
- Understand the meaning, lineage, limitations, and caveats of each dataset, and clearly document assumptions, coverage gaps, definitions, and known quality constraints.
Analyst Enablement \& Data Understanding
- Work closely with analysts and portfolio managers to understand research questions, translate them into data requirements, and deliver well\-documented datasets, extracts, and analyses.
- Dig into the drivers behind trends observed in the data, helping analysts distinguish durable signals from noise, one\-off effects, data artifacts, or coverage changes.
- Surface data\-driven alerts, explainable anomalies, and relevant changes in key metrics where the underlying data quality and business interpretation are well understood.
- Communicate technical findings, data caveats, statistical context, and limitations clearly to both technical and non\-technical stakeholders.
AI Readiness \& Practical AI Use Cases
- Maintain data assets in formats that can be safely and effectively used by analytics tools, LLM applications, AI agents, and retrieval or tool\-based workflows.
- Demonstrate a good conceptual understanding of large language models, AI agents, tool use, retrieval\-augmented generation, embeddings, structured outputs, and prompt\-driven workflows.
- Identify practical AI\-enabled use cases that improve analyst efficiency, such as conversational data exploration, automated research summaries, data quality explanations, metric lookup, and hypothesis triage.
- Partner with technology teams to ensure that AI solutions are grounded in clean, documented, well\-permissioned, and trustworthy data rather than treating AI development as the primary responsibility of the role.
Analytical Methods \& Model Evaluation
- Apply appropriate statistical and time\-series techniques to support KPI forecasting, anomaly detection, trend analysis, and signal evaluation when required by analyst use cases.
- Conduct disciplined backtesting and validation of datasets, signals, and model outputs, with attention to overfitting, data revisions, and signal stability.
- Document model assumptions, evaluation results, confidence ranges, and limitations in a way that supports informed analyst decision\-making.
QUALIFICATIONS
Required
- Hands\-on experience with Python for data engineering and analysis, including pandas, numpy, and data validation techniques.
- Bachelor’s degree in Computer Science, Data Engineering, Statistics, Applied Mathematics, Engineering, or a related quantitative discipline.
- Ability to understand business context, map raw data observations to meaningful metrics, and explain data limitations clearly.
- Familiarity with LLM tooling and interest in applying it to analyst workflows.
- Solid grounding in statistics, time\-series analysis, forecasting, anomaly detection, and disciplined backtesting practices.
- Excellent written and verbal communication skills, with the ability to present data issues, assumptions, and technical findings clearly to analysts and decision\-makers.
- Highly organized, self\-directed, and comfortable maintaining multiple datasets and pipelines in a fast\-paced environment.
Preferred
- Experience working with alternative data vendors, investment research datasets, financial datasets, or other high\-volume third\-party data sources.
- Proven experience building, operating, and maintaining end\-to\-end data pipelines in a production or business\-critical environment.
- Experience preparing datasets for LLM, RAG, agentic analytics, semantic search, or conversational data exploration use cases.
This is a fully onsite 5 days a week role based in our Midtown office.
Benefits: Full medical \& vision, 401(k).
Compensation range is $85,000\-$100,000\.
Email [email protected] with application questions
Pay: $85,000\.00 \- $100,000\.00 per year
Benefits:
- 401(k)
- Dental insurance
- Flexible spending account
- Health insurance
- Life insurance
- Paid time off
- Retirement plan
- Vision insurance
Application Question(s):
- Will you now, or in the future, require sponsorship for employment visa status (e.g. F\-1 OPT or H\-1B visa status)?
Ability to Commute:
- New York, NY 10022 (Required)
Work Location: In person
Salary Context
This $85K-$100K range is in the lower quartile for Data Engineer roles in our dataset (median: $153K across 35 roles with salary data).
Role Details
About This Role
Data Engineers build the pipelines that feed AI models. They design ETL workflows, manage data lakes, and ensure training and inference data is clean, timely, and accessible. Without good data engineering, AI projects fail. It's that simple.
The AI era has expanded the data engineer's scope far beyond batch ETL jobs. You're building real-time embedding pipelines for RAG systems, managing vector databases, ensuring training data quality at scale, and building the infrastructure that lets ML teams iterate on data as fast as they iterate on models. Data quality is the biggest predictor of model quality, and you're the person responsible for it.
Across the 4,317 AI roles we're tracking, Data Engineer positions make up 1% of the market. At Interval Partners, this role fits into their broader AI and engineering organization.
Data Engineer demand in AI contexts is strong and growing. Every company building AI needs clean, reliable data pipelines. The shift toward real-time AI applications (chatbots, recommendation engines, agent systems) means data engineering is more critical than ever. Companies are willing to pay premium salaries for data engineers with AI/ML pipeline experience.
What the Work Looks Like
A typical week includes: debugging a data pipeline that's producing stale embeddings for the RAG system, optimizing a Spark job that processes training data, building a data quality monitoring dashboard, meeting with the ML team to understand their next data requirements, and writing dbt models that transform raw event data into ML-ready features. The work is deeply technical and high-impact.
Data Engineer demand in AI contexts is strong and growing. Every company building AI needs clean, reliable data pipelines. The shift toward real-time AI applications (chatbots, recommendation engines, agent systems) means data engineering is more critical than ever. Companies are willing to pay premium salaries for data engineers with AI/ML pipeline experience.
Skills Required
SQL, Python, and distributed systems (Spark, Airflow, dbt) are core. Cloud data platforms (Snowflake, BigQuery, Redshift) are increasingly standard. Many AI-focused roles also want familiarity with vector databases and embedding pipelines. Understanding data modeling, pipeline orchestration, and data quality frameworks covers the essentials.
AI-specific data engineering skills include: building feature stores, managing training data versioning, implementing data lineage tracking, and building real-time embedding pipelines. Experience with streaming systems (Kafka, Flink) is valuable for real-time AI applications. Understanding ML data requirements (balanced datasets, data augmentation, evaluation set construction) makes you much more effective working with ML teams.
Strong postings specify the data stack, mention ML pipeline work, and describe the scale of data you'll be working with. Look for companies that understand the connection between data quality and model quality. Avoid roles that conflate data engineering with data analysis.
Compensation Benchmarks
Data Engineer roles pay a median of $185,000 based on 83 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400. This role's midpoint ($92K) sits 50% below the category median. Disclosed range: $85K to $100K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Interval Partners AI Hiring
Interval Partners has 1 open AI role right now. They're hiring across Data Engineer. Based in New York, NY, US. Compensation range: $100K - $100K.
Location Context
AI roles in New York pay a median of $220,000 across 1,650 tracked positions.
Career Path
Common paths into Data Engineer roles include Backend Engineer, Database Administrator, Analytics Engineer.
From here, career progression typically leads toward Senior Data Engineer, ML Engineer, Data Platform Lead.
Master SQL and Python first. Then learn a distributed processing framework (Spark or its modern alternatives) and a pipeline orchestrator (Airflow, Dagster, Prefect). Build a portfolio project that demonstrates end-to-end pipeline construction: ingest, transform, validate, serve. If you want to specialize in AI data engineering, add vector databases and embedding pipelines to your skill set.
What to Expect in Interviews
Expect SQL deep-dives (query optimization, partitioning strategies, data modeling), Python coding focused on data pipeline patterns, and system design questions about building scalable ETL workflows. Companies with ML teams will ask about feature stores, embedding pipelines, and training data management. Be ready to discuss data quality monitoring, pipeline orchestration, and how you'd handle schema evolution in a production data lake.
When evaluating opportunities: Strong postings specify the data stack, mention ML pipeline work, and describe the scale of data you'll be working with. Look for companies that understand the connection between data quality and model quality. Avoid roles that conflate data engineering with data analysis.
AI Hiring Overview
The AI job market has 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 roles).
Data Engineer demand in AI contexts is strong and growing. Every company building AI needs clean, reliable data pipelines. The shift toward real-time AI applications (chatbots, recommendation engines, agent systems) means data engineering is more critical than ever. Companies are willing to pay premium salaries for data engineers with AI/ML pipeline experience.
The AI Job Market Today
The AI job market spans 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (138) are outnumbered by mid-level (2,071) and senior (1,655) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $287,500 median, while Prompt Engineer roles sit at $145,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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