- Company Name
- Home.CA
- Job Title
- Senior Data Science Engineer
- Job Description
-
Job title: Senior Data Science Engineer
Role Summary
Design and operationalize end‑to‑end data pipelines, semantic search, and recommendation systems that underpin a real‑time, trustworthy real‑estate decision platform. Lead data architecture decisions, schema strategy, and feature store engineering while ensuring data quality, latency, and cost controls across cloud services.
Expections
* 6+ years of production experience in data engineering and ML system deployment
* Proven expertise in designing scalable ETL/ELT pipelines, data modeling, and schema evolution
* Hands‑on implementation of RAG, vector search, and embedding pipelines at scale
* Strong Python and SQL skills; experience with Java/TypeScript, Node.js, and RESTful APIs
* Deep knowledge of PostgreSQL, NoSQL (Firestore, MongoDB), and distributed storage (BigQuery, BigTable)
* Familiarity with GCP services (Vertex AI, Cloud Monitoring, Kubernetes) and containerization
* Demonstrated focus on data quality, observability, and production‑grade resilience
* Ability to translate business requirements into robust data solutions that impact user experience
Key Responsibilities
1. Architect and maintain ingestion, normalization, and reconciliation pipelines for financial, market, and property data sources.
2. Build and manage feature stores and data lakes, enforcing schema consistency and lineage.
3. Develop embedding pipelines, vector indexes, and semantic search capabilities for multimodal data.
4. Design and implement RAG workflows, retrieval ranking, and relevance metrics.
5. Create user‑centric recommendation engines (property, content, advisors) using collaborative filtering and hybrid techniques.
6. Enforce data quality checks, anomaly detection, and monitoring dashboards.
7. Collaborate with ML teams, platform architects, and product stakeholders to define data strategy and technical roadmaps.
Required Skills
* Data Engineering & Architecture: ETL/ELT, schema design, feature stores, data lakes.
* ML Systems: RAG, vector databases, embedding models, semantic search.
* Programming: Python, SQL, TypeScript/Node.js, Java (Spring Boot).
* Databases: PostgreSQL, Firestore, MongoDB, BigQuery, BigTable.
* Cloud & DevOps: Google Cloud Platform, Docker, Kubernetes.
* Monitoring & Observability: Cloud Monitoring, custom dashboards.
* Soft Skills: strong communication, ownership, production‑first mindset, data integrity focus.
Required Education & Certifications
* Bachelor’s (or higher) degree in Computer Science, Data Science, Engineering, or related field.
* Certifications: Preferred GCP Professional Data Engineer or equivalent; optional ML/AI certifications (e.g., TensorFlow, Vertex AI).