Data Engineer

Are you an experienced Data Engineering professional with a passion for building scalable, reliable, and high-performance data systems? Do you have hands-on experience designing and optimizing end-to-end real-time and batch pipelines, and developing cloud-native data architectures using modern technologies such as AWS, GCP, Azure, Databricks, and Snowflake?

We are looking for a Senior Data Engineer to architect, design, and implement scalable, high-performance data solutions. The ideal candidate will be an expert in at least one major cloud data ecosystem (AWS, Azure, GCP, Snowflake, or Databricks) and possess a deep understanding of the end-to-end data lifecycle, from ingestion to business intelligence. Qualification & Skill Set Requirements Core Technical Competencies Experience: 5+ years of hands-on data engineering experience in a production environment. Languages: Strong proficiency in Python, SQL (complex queries, performance tuning), and PySpark/Apache Spark. Data Modeling: Expert knowledge of data modeling (3NF, Star, Snowflake Schema) and Lakehouse/Warehouse architectures. ETL/ELT & Orchestration: Proven experience building pipelines using tools like dbt, Airflow, Dagster, or native cloud orchestrators (Glue, Data Factory, Composer). Integrations: Experienced in integrating data from diverse sources: APIs, RDBMS/NoSQL databases, flat files, and streaming platforms (Kafka, Kinesis, Pub/Sub). Cloud Platform Expertise (Specialization-Specific) Candidates should demonstrate deep expertise in anyone of the following: Snowflake: SnowSQL, Streams, Tasks, Snowpark, and cost optimization. Databricks: Delta Lake, Unity Catalog, Delta Live Tables (DLT), and Spark optimization. GCP: BigQuery, Dataflow, Dataproc, Pub/Sub, and Cloud Functions. Azure: Synapse Analytics, Data Factory, Azure Databricks, and Stream Analytics. AWS: Redshift, S3, Lake Formation, Glue, and Lambda. Professional Practices SDLC & DevOps: Proficient in 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), and compliance frameworks. Agile: Active experience in Agile/Scrum environments using Jira or Azure Boards. Mentorship: Ability to lead projects and provide technical guidance to junior/mid-level engineers. Responsibilities Architecture: Architect, design, and implement scalable, reliable data solutions and pipelines aligned with business analytics needs. Optimization: Manage and fine-tune cloud resources and workloads for maximum performance, reliability, and cost-efficiency. Data Transformation: Lead the development of ETL/ELT processes for both batch and real-time data processing. Collaboration: Partner with Product, Engineering, and Data Science teams to deliver effective, data-driven solutions. Governance & Quality: Promote and enforce best practices in data governance, security, and data quality frameworks. Mentorship: Provide technical leadership and mentorship to the team, ensuring architecture quality and best practices. Documentation: Maintain comprehensive documentation of data architectures, configurations, and workflows. Fusemachines is an Equal Opportunities Employer, committed to diversity and inclusion. All qualified applicants will receive, Are you an experienced Data Engineering professional with a passion for building scalable, reliable, and high-performance data systems? Do you have hands-on experience designing and optimizing end-to-end real-time and batch pipelines, and developing cloud-native data architectures using modern technologies such as AWS, GCP, Azure, Databricks, and Snowflake?

We are looking for a Senior Data Engineer to architect, design, and implement scalable, high-performance data solutions. The ideal candidate will be an expert in at least one major cloud data ecosystem (AWS, Azure, GCP, Snowflake, or Databricks) and possess a deep understanding of the end-to-end data lifecycle, from ingestion to business intelligence. Qualification & Skill Set Requirements Core Technical Competencies Experience: 5+ years of hands-on data engineering experience in a production environment. Languages: Strong proficiency in Python, SQL (complex queries, performance tuning), and PySpark/Apache Spark. Data Modeling: Expert knowledge of data modeling (3NF, Star, Snowflake Schema) and Lakehouse/Warehouse architectures. ETL/ELT & Orchestration: Proven experience building pipelines using tools like dbt, Airflow, Dagster, or native cloud orchestrators (Glue, Data Factory, Composer). Integrations: Experienced in integrating data from diverse sources: APIs, RDBMS/NoSQL databases, flat files, and streaming platforms (Kafka, Kinesis, Pub/Sub). Cloud Platform Expertise (Specialization-Specific) Candidates should demonstrate deep expertise in anyone of the following: Snowflake: SnowSQL, Streams, Tasks, Snowpark, and cost optimization. Databricks: Delta Lake, Unity Catalog, Delta Live Tables (DLT), and Spark optimization. GCP: BigQuery, Dataflow, Dataproc, Pub/Sub, and Cloud Functions. Azure: Synapse Analytics, Data Factory, Azure Databricks, and Stream Analytics. AWS: Redshift, S3, Lake Formation, Glue, and Lambda. Professional Practices SDLC & DevOps: Proficient in 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), and compliance frameworks. Agile: Active experience in Agile/Scrum environments using Jira or Azure Boards. Mentorship: Ability to lead projects and provide technical guidance to junior/mid-level engineers. Responsibilities Architecture: Architect, design, and implement scalable, reliable data solutions and pipelines aligned with business analytics needs. Optimization: Manage and fine-tune cloud resources and workloads for maximum performance, reliability, and cost-efficiency. Data Transformation: Lead the development of ETL/ELT processes for both batch and real-time data processing. Collaboration: Partner with Product, Engineering, and Data Science teams to deliver effective, data-driven solutions. Governance & Quality: Promote and enforce best practices in data governance, security, and data quality frameworks. Mentorship: Provide technical leadership and mentorship to the team, ensuring architecture quality and best practices. Documentation: Maintain comprehensive documentation of data architectures, configurations, and workflows. 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.

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. Type: Remote Full-time