Senior Data Engineer – Global Multi‑Strategy Investment Firm – Front‑Office – Up
Mondrian Alpha, New York, NY, United States
Senior Data Engineer — Global Multi‑Strategy Investment Firm — Front‑Office — Up to $500k TC
We’re partnering with a compact, high‑impact global investment firm running a multi‑strategy platform across public and private markets. With around 85 people globally (targeting 100 in the near term) and roughly a third technologists, the engineering and data platforms you build will sit directly next to portfolio managers and researchers deploying billions across rates, FX, equities, credit and macro. The mandate is broad, the capital is permanent, and technology is sponsored at the top table — your work directly shapes how risk is taken in global markets.
The firm is expanding its data ecosystem: replacing a senior data engineer who is leaving and adding two more seats. This is a chance to help define the next generation of their data platform rather than inherit a finished stack.
The Opportunity
You’ll be a software engineer focused on data, building and evolving the core data systems that trading, risk and research rely on. You’ll design efficient pipelines, modernise legacy workflows, and move more of the platform onto a cloud‑native, distributed stack (Snowflake, DBT, Spark, Databricks/EMR), while working directly with PMs, quant teams, trading staff and risk managers.
Example problems:
Building robust, low‑latency pipelines that feed position, P&L and risk data into trading and risk dashboards.
Designing golden‑source datasets (security, account and price masters) used across macro and multi‑asset strategies.
Implementing data quality, lineage and observability so PMs can trust the numbers driving their decisions.
Major Responsibilities
Design, develop and optimise data pipelines for trading, alpha generation, research, risk management, accounting and more.
Build new golden‑source datasets (e.g. security master, account master, price master) that are critical to the firm.
Develop shared Python libraries for data APIs, logging and core functionality used by quant and trading teams.
Ensure high data quality and observability using modern data governance and validation tools.
Optimise large‑scale data processing workflows for efficiency and performance using
Spark
and related technologies.
Support and troubleshoot data pipelines, APIs and database performance issues in production.
Requirements
6 years of development experience, including 2 years focused on data engineering.
Excellent
Python
and
SQL
skills for data processing and automation, with strong software‑engineering fundamentals (testing, code review, design).
Extensive ETL/ELT experience, including distributed processing with
Spark
(Databricks or EMR experience strongly preferred).
Strong understanding of data structures, modelling, efficient query design and performance tuning in SQL databases (Postgres or MS SQL Server).
Experience building and deploying containerised applications (Docker, Kubernetes) in cloud environments.
Familiarity with Snowflake and DBT, plus modern data catalogue / data quality tooling.
Great communication skills and comfort working directly with both technical and non‑technical stakeholders.
Financial markets experience is a plus, not a prerequisite — they have successfully hired engineers from big‑tech environments before. What matters most is depth in software engineering and data systems, plus curiosity about markets.
Preferred Skills
Hands‑on experience with Snowflake, Databricks or similar cloud data platforms.
Market data literacy across equities, fixed income, futures and options.
Experience with data observability tools (e.g. OpenMetadata, Great Expectations) and BI dashboards.
Experience building data‑centric APIs in Python (FastAPI or similar).
What They’re Not Looking For
Pure managers with little recent hands‑on IC work.
Narrow, tool‑centric profiles (Airflow/DBT/SQL only) without solid engineering fundamentals.
This is an opportunity to join a lean, senior environment where you own systems end‑to‑end and see your work reflected directly in how billions of dollars are risked across global markets.
#J-18808-Ljbffr
We’re partnering with a compact, high‑impact global investment firm running a multi‑strategy platform across public and private markets. With around 85 people globally (targeting 100 in the near term) and roughly a third technologists, the engineering and data platforms you build will sit directly next to portfolio managers and researchers deploying billions across rates, FX, equities, credit and macro. The mandate is broad, the capital is permanent, and technology is sponsored at the top table — your work directly shapes how risk is taken in global markets.
The firm is expanding its data ecosystem: replacing a senior data engineer who is leaving and adding two more seats. This is a chance to help define the next generation of their data platform rather than inherit a finished stack.
The Opportunity
You’ll be a software engineer focused on data, building and evolving the core data systems that trading, risk and research rely on. You’ll design efficient pipelines, modernise legacy workflows, and move more of the platform onto a cloud‑native, distributed stack (Snowflake, DBT, Spark, Databricks/EMR), while working directly with PMs, quant teams, trading staff and risk managers.
Example problems:
Building robust, low‑latency pipelines that feed position, P&L and risk data into trading and risk dashboards.
Designing golden‑source datasets (security, account and price masters) used across macro and multi‑asset strategies.
Implementing data quality, lineage and observability so PMs can trust the numbers driving their decisions.
Major Responsibilities
Design, develop and optimise data pipelines for trading, alpha generation, research, risk management, accounting and more.
Build new golden‑source datasets (e.g. security master, account master, price master) that are critical to the firm.
Develop shared Python libraries for data APIs, logging and core functionality used by quant and trading teams.
Ensure high data quality and observability using modern data governance and validation tools.
Optimise large‑scale data processing workflows for efficiency and performance using
Spark
and related technologies.
Support and troubleshoot data pipelines, APIs and database performance issues in production.
Requirements
6 years of development experience, including 2 years focused on data engineering.
Excellent
Python
and
SQL
skills for data processing and automation, with strong software‑engineering fundamentals (testing, code review, design).
Extensive ETL/ELT experience, including distributed processing with
Spark
(Databricks or EMR experience strongly preferred).
Strong understanding of data structures, modelling, efficient query design and performance tuning in SQL databases (Postgres or MS SQL Server).
Experience building and deploying containerised applications (Docker, Kubernetes) in cloud environments.
Familiarity with Snowflake and DBT, plus modern data catalogue / data quality tooling.
Great communication skills and comfort working directly with both technical and non‑technical stakeholders.
Financial markets experience is a plus, not a prerequisite — they have successfully hired engineers from big‑tech environments before. What matters most is depth in software engineering and data systems, plus curiosity about markets.
Preferred Skills
Hands‑on experience with Snowflake, Databricks or similar cloud data platforms.
Market data literacy across equities, fixed income, futures and options.
Experience with data observability tools (e.g. OpenMetadata, Great Expectations) and BI dashboards.
Experience building data‑centric APIs in Python (FastAPI or similar).
What They’re Not Looking For
Pure managers with little recent hands‑on IC work.
Narrow, tool‑centric profiles (Airflow/DBT/SQL only) without solid engineering fundamentals.
This is an opportunity to join a lean, senior environment where you own systems end‑to‑end and see your work reflected directly in how billions of dollars are risked across global markets.
#J-18808-Ljbffr