Senior Data Engineer (LLM)

Sunscrapers is a technology consultancy that empowers finance and healthcare leaders to succeed by leveraging cutting-edge software, data, and AI.

We combine world-class engineering, deep industry expertise, and proprietary know-how to deliver innovative, high-impact solutions. Specializing in software engineering, DevOps, data engineering, and data science, we design and build AI-powered data platforms and web applications tailored to each client’s unique needs.

Trusted by over 60 clients across the US, UK, and beyond, we consistently maintain a 4.9/5 client satisfaction rating, with partnerships averaging five years or more.

The project:

We are carrying out the project for our client, an American private equity and investment management fund - listed on the Forbes 500 list - based in New York.

We support them in the area of the infrastructure and data platform, and very recently we also build and experiment with Gen AI applications. The client operates very widely in the world of finance, loans, investments and real estate.

As a Senior Data Engineer you’ll design and implement core systems that enable data science and data visualization at companies that implement data-driven decision processes to create a competitive advantage. 

You’ll build data platform for data and business teams, including internal tooling, data pipeline orchestrator, data warehouses and more, using:

Technologies: Python, Terraform, SQL, Pandas, Shell scripts

Tools: git, Docker, Snowflake, Pinecone, Neo4j, Jenkins, Jupyter Notebook, OpenAI API, Apache Airflow / Astronomer, Kubernetes, Artifactory, Windows with WSL, Linux, Gitlab

AWS: EC2, ELB, IAM, RDS, Route53, S3, and more

Best Practices: Continuous Integration, Code Reviews

The ideal candidate will be well organized, eager to constantly improve and learn, driven and, most of all - a team player!

Your responsibilities will include:

• Developing PoCs using latest technologies, experimenting with third party integrations
• Delivering production grade applications once PoCs are validated
• Creating solutions that enable data scientists and business analysts to be self-sufficient as much as possible.
• Finding new ways how to leverage Gen AI applications and underlying vector and graph data storages
• Designing datasets and schemes for consistency and easy access
• Contributing data technology stacks including data warehouses and ETL pipelines
• Building data flows for fetching, aggregation and data modeling using batch and streaming pipelines
• Documenting design decisions before implementation