Data Engineer

Role: Data Engineer (Investment & Quantitative Research)

Location: Abu Dhabi, UAE

Department: Investment Technology / Data Platforms

Type: Full-time

Role Overview

We are seeking a highly skilled Data Engineer to support quantitative research, portfolio analytics, and investment strategy teams by building scalable data pipelines and managing diverse alternative datasets. The ideal candidate will have experience in modern data engineering practices, strong programming capability, and a passion for enabling data-driven decision-making within a sophisticated investment environment.

Key Responsibilities

• Design, build, and maintain robust, scalable data pipelines for structured, semi-structured, and unstructured datasets.
• Work closely with Quantitative Researchers, Data Scientists, and Investment teams to deliver clean, reliable data for model development and research workflows.
• Ingest and integrate alternative data sources (e.g., satellite imagery, geospatial feeds, consumer transaction data, ESG datasets, web-scraped data, foot-traffic metrics).
• Develop and maintain metadata, data catalogs, and quality control frameworks to ensure data lineage and transparency.
• Implement automated data validation, cleansing, and anomaly-detection processes.
• Build and optimize cloud-native data pipelines (e.g., using Spark, Databricks, Snowflake, AWS/GCP/Azure tooling).
• Collaborate with platform engineers to enhance the underlying data infrastructure and ensure high availability and performance.
• Support compliance, governance, and secure access protocols for sensitive datasets.

Required Qualifications

• Bachelor's or Master's degree in Computer Science, Engineering, Data Science, or a related field.
• 48+ years of data engineering experience within financial services, technology, or large-scale data environments.
• Strong proficiency in Python and/or Scala, plus SQL.
• Hands-on experience with distributed data processing frameworks (Spark or similar).
• Familiarity with cloud data platforms (AWS, GCP, Azure).

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  Experience working with alternative datasets and handling large-volume, high-frequency data.
• Solid understanding of data warehousing concepts and ETL/ELT workflows.

Preferred Skills

• Experience supporting Quant teams, research workflows, or systematic investment groups.
• Knowledge of machine learning data pipelines or feature engineering frameworks.
• Exposure to financial markets (equities, fixed income, macro, or alternatives).
• Experience with workflow orchestration tools (Airflow, Prefect, Dagster).
• Proficiency with CI/CD, containerization (Docker), and infrastructure-as-code.

Soft Skills

• Strong communication and stakeholder management.
• Ability to translate research and investment needs into technical requirements.
• Comfortable working in a fast-paced, data-rich environment.
• Strong problem-solving mindset and high attention to detail.