Lead Data Analytics Engineer

We're looking for a data professional who can turn messy datasets into production-ready analytics products. You must have strong data analytics skills, proficiency in SQL, and hands-on experience with cloud data warehouses (BigQuery preferred). You should be comfortable building and maintaining data pipelines, working with data scientists and data engineers to productionize data product, and thinking critically about data quality and costs.

Domain knowledge in US mortgage/real estate is a plus, but we value technical depth and the ability to quickly learn new domains. If you thrive on exploratory analysis, can collaborate across technical and business teams, and want to work on ML-powered data products at scale, we want to hear from you. You shouldn't apply if you only have experience on just BI/reporting analytics or purely focused on data science without interest in data analytics/engineering or feel uncomfortable working with ambiguous problems.

Knowledge and Experience

Candidates should have experience in the following areas, with demonstrably deep expertise in a few:

• Strong exploratory data analysis skills in SQL and at least one of R, Python, or JavaScript.

• Familiarity with databases and analytical tools that use SQL as a data manipulation language (especially Google BigQuery).

• It will be helpful to have working knowledge of DBT (Data Build Tool) or similar SQL templating systems, as well as DAG orchestration tools like Airflow.

• A strong interest in the application of AI models, including generative AI/LLMs and novel AI architectures, to real data problems. We use LLMs in data processing pipelines and train/deploy our own AI and machine learning models for product-specific features.

• Experience with projecting, measuring, and controlling cloud computing costs for product workflows is important. Designing products with cost considerations in mind is crucial because of the size and complexity of our product data pipelines.

• Experience analyzing US mortgage and residential real estate data. We care about the combination of subject-matter knowledge and clear understanding of the privacy and security requirements involved.

• Good collaboration with engineering teams across the lifecycle of product ideation, prototyping, and productionizing. Some prior experience as a software/data engineer or machine learning engineer is strongly preferred.

• You must be comfortable using git version control in a collaborative working environment.

• Building your own ETL pipelines will not be a routine responsibility, but be ready to roll up your sleeves in a pinch. Critical thinking about ETL requirements for time series data and backtesting requirements (what did we know, when, and can we reproduce that state of knowledge?) is an important skill.

• Some prior experience with data visualization and statistical or ML modeling will help you succeed. If you do not have experience, you should be excited to learn this on the job.