Senior Data Engineer

About the Role Fusemachines is a global provider of enterprise AI products and services. Founded in 2013, the company helps democratize AI and supports clients across North America, Asia, and Latin America. As a Senior Data Engineer, you will architect, design, and implement scalable, high-performance data solutions that power real-time and batch data pipelines for AI-enabled transformations. What You'll Do

• Architect, design, and implement scalable, high-performance data solutions that handle both real-time and batch data pipelines.
• Design and optimize end-to-end data pipelines and cloud-native data architectures using AWS, GCP, Azure, Databricks, Snowflake.
• Own the end-to-end data lifecycle from ingestion to business intelligence, including ingestion, transformation, storage, and orchestration.
• Build pipelines using dbt, Airflow, Dagster, or native cloud orchestrators (Glue, Data Factory, Composer).
• Integrate data from diverse sources: APIs, RDBMS/NoSQL, flat files, and streaming platforms (Kafka, Kinesis, Pub/Sub).
• Collaborate with stakeholders to translate business requirements into data products; ensure data quality, governance, and security (RBAC, encryption).
• Stay current with cloud data ecosystem tooling and best practices; contribute to data modeling (3NF, Star, Snowflake Schema) and Lakehouse/Warehouse architectures. What We're Looking For
• 5+ years of hands-on data engineering experience in a production environment.
• Strong proficiency in Python, SQL (complex queries, performance tuning), and PySpark/Apache Spark.
• Expert knowledge of data modeling (3NF, Star, Snowflake Schema) and Lakehouse/Warehouse architectures.
• Proven experience building pipelines with dbt, Airflow, Dagster, or native cloud orchestrators (Glue, Data Factory, Composer).
• Experience integrating data from diverse sources: APIs, RDBMS/NoSQL, flat files, and streaming platforms (Kafka, Kinesis, Pub/Sub).
• Deep expertise in at least one major cloud data ecosystem (Snowflake, Databricks, GCP, Azure, or AWS).
• SDLC & DevOps: Git workflows, CI/CD pipelines (GitHub Actions, Azure DevOps, AWS CodePipeline), and IaC (Terraform/CloudFormation).
• Data Governance: strong understanding of data quality, lineage, observability, security (RBAC, encryption). Nice to Have
• Snowflake features: SnowSQL, Streams, Tasks, Snowpark, and cost optimization.
• Databricks: Delta Lake, Unity Catalog, Delta Live Tables (DLT), and Spark optimization.
• Experience with cloud services across AWS, Azure, GCP (e.g., Redshift, S3, Lake Formation, Glue, Lambda; Synapse, Data Factory, Databricks; BigQuery, Dataflow, Pub/Sub, Cloud Functions).
• Familiarity with data governance, cost optimization, and security best practices in data architectures.