Senior Data Engineer

Functional / Industry Experience: Sr. Data Engineer (5 – 10 years) Position Type (Core/Consultant/BOD): Core Time Commitment: Full-Time Summary 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. Responsibilities 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. Sample Projects You Work On 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. Your Experience 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 DevOps 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. Infrastructure as Code (Nice to have): Production experience with declarative infrastructure definition – e.g. Terraform, Pulumi or similar. Professional Experience & Education Bachelor's or Master's degree in Computer Science, Engineering, or a related field. Relevant certification from Azure, AWS, GCP, IBM, Snowflake, dbt. Other Requirements Cross industry exposure and experience preferred. 5 days a week in‑office: We believe in the power of in‑person collaboration to drive innovation, mentorship, and velocity. This role is based in our office five days a week – ideal for those who thrive in high‑energy, team‑centric environments. Global exposure: Be ready to travel! This role offers occasional cross‑continental and international travel opportunities, giving you the chance to engage with global teams, portfolio companies, and cutting‑edge tech initiatives around the world. Work Authorization We are unable to sponsor or transfer employment visas at this time. Company Description Established in 1992, Cerberus Capital Management, L.P., together with its affiliates, is one of the world’s leading private investment firms. Through its team of investment and operations professionals, Cerberus specializes in providing both financial resources and operational expertise to help transform undervalued and underperforming companies into industry leaders for long‑term success and value creation. Cerberus holds controlling or significant minority interests in companies around the world. Cerberus Technology Solutions is an operating company and subsidiary of Cerberus Capital Management. We are a new, but growing team of AI specialists – data scientists, software engineers, and technology strategists – working to transform how an alternative investment firm with $65B in assets under management leverages technology and data. Our remit is broad, spanning investment operations, portfolio companies, and internal systems, giving the team the opportunity to shape the way the firm approaches analytics, automation, and decision‑making. We operate with the creativity and agility of a small team, tackling diverse, high‑impact challenges across the firm. While we are embedded within a global investment platform, we maintain a collaborative, innovative culture where our AI talent can experiment, learn, and have real influence on business outcomes. #J-18808-Ljbffr