We are seeking a strong Data Engineer who can own and evolve the data foundations supporting our investment processes. You will engineer the data pipelines, models, and systems that power decision-making across portfolio management, risk, research, and attribution. This role is ideal for someone who excels at data engineering in a highly analytical, investment-focused environment and wants to work directly with investment professionals and quantitative teams.
Role:
You will design, build, and maintain scalable, cloud-ready data pipelines and infrastructure enabling investment business teams—research analysts, quantitative modelers, traders, risk managers, and portfolio managers—to access accurate, timely, and high-quality data. The role blends engineering rigor with fluency in investment data, helping power analytics, models, and mission-critical investment applications.
Key Responsibilities:
Data Pipeline & Engineering Development
Build and optimize end-to-end data pipelines to support ingestion, scrubbing, transformation, and distribution of investment data.
Implement robust ETL/ELT processes for structured and unstructured datasets across fixed income, Equities, and Multi asset domains.
Data Modeling, Architecture & Quality Control
Develop, maintain, and document data models, schemas, and database structures supporting investment analytics and operational workflows.
Implement data validation and quality frameworks aligned with investment teams’ data integrity standards.
Investment Data Domain Expertise:
Build fluency across various investment data domains and design data access patterns that deliver clean, consistent, and efficient access to consumers.
Understand how key model inputs (terms and conditions, pricing, index attributes, reference data) influence analytical outputs and decision-making.
Systems Integration & API Development
Integrate data with internal platforms and external vendors; streamline and standardize access through APIs, warehouses, or curated data layers.
Support enhancements to data streams and contribute to cloud-native data engineering initiatives aligned with machine learning and analytics engineering trends.
Enable seamless data access through analytics and visualization tools (e.g., Power BI, Excel, and BQL), ensuring that curated datasets, semantic models, and governed views are optimized for consumption by investment teams and downstream reporting workflows.
Collaboration & Cross-Functional Partnership
Partner with investment teams to understand requirements and deliver high-quality data that supports research, risk, and portfolio-management workflows.
Contribute to documentation, data governance, and best practices across the technology and investment organization.
Make Data AI-Ready
Design and structure datasets and documents so AI assistants (e.g., Copilot, ChatGPT) can seamlessly interpret and generate on-the-fly access requests for citizen developers.
Standardize metadata, tagging, and ontologies to reduce ambiguity in natural-language queries and improve AI-driven retrieval accuracy.
Implement data normalization, consistent schemas, and well-defined relationships to support LLM-driven reasoning across investment and operational domains.
Build and maintain semantic layers or knowledge catalogs that allow AI models to map user intent to underlying data assets.
Partner with architecture, security, and business teams to ensure AI-facing datasets meet governance, privacy, and compliance requirements.
Develop automated pipelines that prepare and refresh AI-ready data—including embeddings, vector indexes, and domain-specific feature stores.
Evaluate and optimize data structures for compatibility with enterprise GenAI tools (e.g., Microsoft Copilot, RAG systems, internal LLMs).
Required Skills & Qualifications:
Technical Skills:
Languages & Processing
Pipelines & Lakehouse
Databricks, Spark; orchestration with Azure Data Factory / Airflow
Formats & storage: Delta Lake, Parquet; serving on Synapse / Fabric / Snowflake
Streaming
AI-Ready Data
Ontologies/semantic layer, knowledge catalogs; embeddings & vector search
RAG patterns and alignment with Microsoft Copilot / enterprise LLMs
Data & Investment Knowledge:
Soft Skills
Intellectual curiosity, strong work ethic, and professional presence.
Excellent problem-solving skills and the ability to work across diverse technical and business teams.
Ability to communicate complex data concepts to investment stakeholders.
We look forward to reviewing your profile.
Talent Team @ SIALTP
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