Senior Data Engineer (Databricks/AWS)

About Fusemachines

Fusemachines is a 12+ year old AI company, dedicated to delivering state-of-the-art AI products and solutions to a diverse range of industries. Founded by Sameer Maskey, Ph.D., an Adjunct Associate Professor at Columbia University, our company is on a steadfast mission to democratize AI and harness the power of global AI talent from underserved communities. With a robust presence in four countries and a dedicated team of over 400 full-time employees, we are committed to fostering AI transformation journeys for businesses worldwide. At Fusemachines, we not only bridge the gap between AI advancement and its global impact but also strive to deliver the most advanced technology solutions to the world.

Type: Full-time, Remote
 

About the role

This is a full-time, high-impact position for a Senior Data Engineer with expertise in Databricks, dbt, and Apache Airflow to support a critical CRM data architecture migration for a key client in the Life Sciences industry.

In this role, you will join an urgent initiative to backfill key engineering capabilities and maintain momentum during an ongoing CRM system transition. The project involves migrating enterprise customer data from Veeva CRM to Salesforce Life Sciences Cloud, integrated with an underlying AWS S3 cloud environment and Databricks data warehouse. Your main focus will be building out, configuring, and redirecting data ingestion pipelines out of Life Sciences Cloud into the data warehouse, while implementing dbt models and Airflow orchestrations to ensure complete data accuracy.

Candidates must be able to operate strictly on US East Coast business hours (location is flexible across North America, LATAM, or remote with full Eastern Time overlap).

Qualification / Skill Set Requirement:

• Core Technical Expertise:

  • 5+ years of hands-on data engineering experience with deep expertise in AWS, Databricks, dbt, and Apache Airflow.

  • Strong programming proficiency in Python / PySpark and Advanced SQL (complex joins, analytical window functions).

  • Hands-on expertise in Databricks platform architecture, Lakehouse implementation, Delta Lake, Unity Catalog, and cluster performance tuning.

• Architecture & Migration:

  • Proven track record of architecting and executing migrations.

  • Demonstrated experience scaling platform performance.

• Pipeline Orchestration & Modeling:

  • Proven experience building scalable transformations pipelines using dbt for data transformation, testing, and documentation.

  • Solid background orchestrating complex workflow DAGs with Apache Airflow.

  • Experience working with AWS cloud infrastructure, specifically AWS S3 as an underlying data lake storage layer.

• CRM Integration & Domain Knowledge:

  • Hands-on experience developing integrations and data ingestion pipelines for CRM platforms, specifically Salesforce, Salesforce Life Sciences Cloud, and/or Veeva CRM.

  • Understanding of data structures, customer master data, and analytics workflows within the Life Sciences.

• DevOps & Governance:

  • Deep understanding of SDLC/Agile and DevOps for CI/CD and artifact management.

  • Knowledge of AWS and Databricks security best practices and compliance standards.

• Certifications Preferred: Databricks Certified Data Engineer Associate/Professional, Databricks Spark Developer, and major cloud certifications in AWS.

• Logistics & Shift Overlap:

  • Ability to maintain 100% full working time overlap with US East Coast business hours (ET). Flexible location (US, Canada, LATAM, or remote ET).

Responsibilities

• Pipeline Development & Integration: Architect, build, and deploy data integration pipelines connecting Salesforce Life Sciences Cloud to the client’s Databricks warehouse environment.

• CRM Migration Support: Execute pipeline modifications to transition legacy data feeds from Veeva CRM to Salesforce Life Sciences Cloud, updating warehouse models accordingly.

• Transformation & Workflow Orchestration: Write clean, modular dbt transformation models and organize end-to-end DAG execution using Apache Airflow.

• Data Warehouse & Storage Optimization: Manage Delta tables and optimize Databricks clusters and AWS S3 storage for high performance and cost efficiency.

• Data Validation & Quality Assurance: Implement data quality testing, schemas, and verification rules in dbt and Python to guarantee accurate data delivery.

• Monitoring & Alerting: Build and enforce proactive monitoring frameworks.

• Agile Collaboration: Work closely with project leads, solution architects, and technical stakeholders during US East Coast hours to ensure rapid iteration and goal completion.

Equal Opportunity Employer: Race, Color, Religion, Sex, Sexual Orientation, Gender Identity, National Origin, Age, Genetic Information, Disability, Protected Veteran Status, or any other legally protected group status.