Data Engineer - SQL/Python (5-8 yrs)

Responsibilities :

- Build and own data pipelines that power the quantitative research system. Get data from internal Design, develop and implement ML/AI solutions for real estate markets.

- Find the needle in the haystack - identify the signals that are most descriptive and predictive of market performance.

- Train time series forecasting and cross-sectional ranking models across hundreds of geographies and property types.

- Use these data-driven views to construct risk-efficient model portfolios.

- Help the team build automated processes to support the quant research system using best practices in data engineering, cloud computing, and software development.

- Communicate complex quantitative ideas and insights to investment teams (analysts, PMs, underwriters, etc. ) to improve the adoption of solutions delivered by the data science team.

- Scope, lead, and execute research projects with clearly defined deliverables and timelines.

- Provide input to leadership to support the development of a long-term roadmap.

Requirements :

- Bachelor's degree in computer science, engineering, or a quantitative discipline.

- Minimum of 5 years of experience as a software engineer, data engineer, or ML engineer.

- Expert-level knowledge in building and maintaining data pipelines and data warehouses (especially Snowflake).

- Highly proficient in database design, data modeling, and basic admin tasks.

- Proficient in SQL, python, and common data science libraries (e. g. pandas, numpy, scikitlearn).

Experience :

- Experience with financial data, building end-to-end systems (data, models, UI) driving investment decisions.

- Project management experience.

- Proficient with cloud computing.

- Working with virtual machines, serverless functions, cloud storage, data pipelines, etc. Azure is the primary cloud, and AWS/GCP is also OK.

- Experience with Snowflake and ETL tools like Matillion and Azure Data Factory.

- Experience as a data analyst, data scientist, or BI developer. Able to switch roles based on the team's current needs.