Senior Data Engineer Job Paterson area,New Jersey USA,Software Development

Country:
United States

Format:
On-site

Job posted: 1 day(s) ago

Svitla Systems Inc. is looking for a Senior Data Engineer for a full-time position (40 hours per week) in the USA (office 5 days/week in NJ, NY, CT). Our client is an investment management firm.

The Data Platform team manages data pipelines, web scrapes, the data warehouse, and data models préd across אופ. You'll join a team of brilliant, highly dedicated individuals to build and maintain a next-gen, greenfield data platform for the hedge fund. You will work closely with investment, risk, trading, data science, and technology teams to build a robust, flexible data platform and data warehouse that serves as the single source of truth for our investment ecosystem.

It is a hands‑on engineering role for someone who loves data modeling్ల, understands how a hedge fund "ticks" from front to back, and wants to own critical production systems end‑to‑end. You will enable best‑in‑class data engineering practices, drive the approach to modeling and using investment data, and build data APIs, backend systems, and data models that directly support portfolio managers, risk, trading, and alternative data research.

Requirements:

5+ years of strong experience in software & data engineering, data modeling, and data architecture, ideally on the buy side (hedge fund, asset manager, or multi‑strategy platform).
Deep knowledge of Python and SQL.
Proven experience building production‑grade data pipelines and warehousing solutions.
Strong understanding and hands‑on experience with core investment data domains: security master and reference data (identifiers, classifications, corporate actions), transactions, positions, holdings, and exposures, P&L and performance (including reconciliation concepts), market data, and pricing.
Experience building scalable data pipelines and data platforms using Snowflake, Spark, dbt, Airflow, Dagster, and AWS (or equivalent cloud stack).
Strong understanding of normalized and dimensional data modeling, partitioning/clustering, auditing, and performance tuning.
Demonstrated data quality mindset. Experience designing and implementing validation checks, reconciliation logic, and monitoring for positions and P&L.
Ability to communicate technical concepts clearly to diverse audiences (investors, risk, trading, data science, technology) and to document systems and data contracts.
Experience working for hedge funds, investment companies, banks, fintech, etc.
Experience working in highly regulated financial environments.

Responsibilities:

Own core investment data domains, design and evolve data models for:

Security master and instrument taxonomy across asset classes.

Transactions, positions, exposures, and holdings across portfolios and strategies.

P&L and returns (intraday and end‑of‑day, attribution‑ready).

Market data and benchmarks required for risk and performance.

Define and enforce data modeling standards (naming, normalization, SCD strategy, partitioning, performance).

Build and operate production data pipelines.

Design, implement, and maintain scalable batch data pipelines to ingest, cleanse, normalize, and reconcile data from many internal and external data sources.

Implement robust data quality, lineage, and reconciliation processes for transactions, positions, and P&L.

Continuously improve data by adding new sources, coding business rules, and producing new metrics that support investment, trading, and risk decisions.

Build reusable data services and APIs that expose curated investment datasets to downstream consumers (portfolio managers, risk, quantitative research, dashboards, and external platform providers).

Partner with the Quantitative Research/Risk LPG to package and disseminate data and analytics to the investment team via e‑mail, dashboards, and integrated tools.

Nice to have:

Experience with alternative data (modeling and integrating it alongside traditional data) is a strong plus.

We offer:

US and EU projects based on advanced technologies.
니다competitive compensation based on skills and experience.
Flexibility in workspace, either remote or our welcoming office.
Bonuses for article writing, public talks, and other activities.
Free tech…


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