Senior Data Engineer

About Fusemachines

Founded in 2013, Fusemachines is a global provider of enterprise AI products and services, on a mission to democratize AI. Leveraging proprietary AI Studio and AI Engines, the company helps drive the clients’ AI Enterprise Transformation, regardless of where they are in their Digital AI journeys. With offices in North America, Asia, and Latin America, Fusemachines provides a suite of enterprise AI offerings and specialty services that allow organizations of any size to implement and scale AI. Fusemachines serves companies in industries such as retail, manufacturing, and government.

Fusemachines continues to actively pursue the mission of democratizing AI for the masses by providing high-quality AI education in underserved communities and helping organizations achieve their full potential with AI.

About the role

This is a remote, full-time consulting position (contract), responsible for designing, building, and maintaining the infrastructure required for relationship-centric data integration, storage, and advanced analytics.

We are seeking an experienced Graph Data Engineer to implement Google Cloud’s Unified Graph Fabric and strengthen a BigQuery-based "Customer 360" Knowledge Graph. The ideal candidate will move beyond traditional tabular reporting to analyze the "web of influence" within data, designing property graphs on BigQuery, implementing large-scale graph algorithms, and ensuring performant, cost-efficient graph queries, generating the corresponding pipelines that include AI.

Qualification & Experience

• Education: Must have a full-time Bachelor's degree in Computer Science, Data Engineering, or similar from a top-tier school.

• Experience: 8+ years of experience in data engineering roles, with 3+ years on Google Cloud Platform (GCP).

• Graph Expertise: Proven experience building production-grade data pipelines for use cases such as Identity Resolution, Knowledge Graph development, or Fraud Detection.

• Certifications: Google Cloud Professional Data Engineer certification is highly preferred.

Required skills/Competencies

• Core GCP Data Engineering: 8+ years of real-world development experience with expert knowledge of BigQuery (including Dremel engine) and Cloud Spanner.

• Graph Languages & SQL: Proficiency in GoogleSQL, including standard-compliant graph pattern matching (SQL/PGQ and ISO GQL), and writing complex relationship-traversal logic.

• Graph Architectures: Deep understanding of the trade-offs between Index-Free Adjacency (native graph stores) and Columnar Joins/Scans (BigQuery), and expertise in modeling using Adjacency Lists vs. Nested Fields.

• Python for Graphs: Expert knowledge in Python, specifically using BigQueryDataFrames (BigFrames), NetworkX, and GPU-accelerated backends like RAPIDS cuGraph.

• Orchestration & CI/CD: Strong experience with dbt (preferred) and Prefect or Apache Airflow (Cloud Composer) for orchestrating complex task flows.

• Data Governance: Good understanding of data governance frameworks, including experience with Data Catalog and lineage tracking for graph features.

Responsibilities:

• Graph Schema Implementation: Define and manage property graph schemas using declarative DDL (CREATE PROPERTY GRAPH) directly on existing relational tables and views in BigQuery and Cloud Spanner.

• Query Development & Feature Engineering: Build reusable graph-query patterns (multi-hop traversal, neighborhood signals) and feature engineering pipelines for "Customer 360" analytics, utilizing SQL/GQL and the GRAPH_TABLE operator.

• Global Analytics Execution: Implement and operationalize key graph algorithms: including PageRank, Community Detection, Shortest Path, and Centrality, using BigQuery DataFrames and native dispatching mechanisms.

• Pipeline Automation: Design and schedule automated transformation pipelines (ELT) to prepare "graph-ready" data and move insights from BigQuery back to operational layers (e.g., Spanner Graph) via Reverse ETL.

• Performance Optimization: Optimize query execution to avoid "referenced data" billing traps by implementing advanced partitioning, clustering, and cost-management strategies.

• Collaboration: Work with Product, Engineering, and Data Science teams to understand data requirements and deliver graph-derived features for downstream machine learning models.
  
  Fusemachines is an Equal Opportunities Employer, committed to diversity and inclusion. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, or any other characteristic protected by applicable federal, state, or local laws.