Senior Data Engineer

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. Originally posted on Himalayas