Senior Data Engineer

Functional / Industry

Experience:

Sr. Data Engineer (5 – 10 years)

Position Type (Core/Consultant/BOD): Core

Time Commitment: Full-Time

We are looking to expand our Data Engineering team to build modern, scalable data platforms for our internal investment desks and portfolio companies. You will contribute to the firm’s objectives by delivering rapid and reliable data solutions that unlock value for Cerberus desks, portfolio companies, and other businesses. You’ll do this by designing and implementing robust data architectures, pipelines, and workflows that enable advanced analytics and AI applications.

You may also support initiatives such as due diligence and pricing analyses by ensuring high-quality, timely data availability.

• Design, build, and maintain scalable, cloud-based data pipelines and architectures to support advanced analytics and machine learning initiatives.
• Develop robust ELT/ELT workflows using tools like Snowflake, MS Fabric, dbt, ADF, Airflow, and relational DBs such as SQL Server and PostgreSQL to transform raw data into high-quality, analytics‑ready datasets.
• Collaborate with data scientists, analysts, and software engineers to ensure seamless data integration and availability for predictive modeling and business intelligence.
• Optimize data storage and processing in Azure environments for performance, reliability, and cost‑efficiency.
• Implement best practices for data modeling, governance, and security across all platforms.
• Troubleshoot and enhance existing pipelines to improve scalability and resilience.

Financial Asset Management Pipeline:
Build and manage data ingestion from third‑party APIs, model data using dbt, and support machine learning workflows for asset pricing and prediction using Azure ML Studio. This includes ELT processes, data modeling, running predictions, and storing outputs for downstream analytics.

We’re a small, high‑impact team with a broad remit and diverse technical backgrounds. We don’t expect any single candidate to check every box below – if your experience overlaps strongly with what we do and you’re excited to apply your skills in a fast‑moving, real‑world environment, we’d love to hear from you.

• Strong technical foundation: Degree in a STEM field (or equivalent experience) with hands‑on experience in production environments, emphasizing performance optimization and code quality.
• Python expertise: Advanced proficiency in Python for data engineering, data wrangling and pipeline development.
• Cloud Platforms: Hands‑on experience working with Azure. AWS experience is considered, however Azure exposure is preferred.
• Data Warehousing: Proven expertise with Snowflake – schema design, performance tuning, data ingestion, and security.
• Workflow Orchestration: Production experience with Apache Airflow (Prefect, Dagster or similar), including authoring DAGs, scheduling workloads and monitoring pipeline execution.
• Data Modeling: Strong theoretical and practical understanding of relational and dimensional modeling. Proficient in dbt, including writing modular SQL transformations, building data models, and maintaining dbt projects.
• SQL Databases: Extensive experience with PostgreSQL, SQL Server (or similar), including schema design, optimization, and complex query development.
• Version Control and CI/CD: Familiarity with Git‑based workflows and continuous integration / deployment practices (experience with Azure Dev Ops or Github Actions) to ensure seamless code integration and deployment processes.
• Communication and problem‑solving skills: Ability to articulate complex technical concepts to technical and non‑technical stakeholders alike. Excellent problem‑solving skills with a strong analytical mindset.
• I…