Database Engineer - onsite

Responsibilities

• Data Warehouse Architecture & Design
• Design, implement, and maintain enterprise‑grade data warehouse architectures
• Develop and manage dimensional data models (star, snowflake, fact and dimension tables)
• Translate business and analytical requirements into scalable data schemas
• Support historical, slowly changing dimensions (SCD), and aggregations
• Database Development & Optimization
• Write and optimize complex SQL queries, views, materialized views, and stored procedures
• Tune database performance for large‑scale analytical workloads
• Implement partitioning, indexing, clustering, and distribution strategies
• Ensure high availability, backup, recovery, and disaster‑recovery readiness
• Support and optimize ELT/ETL pipelines feeding the data warehouse
• Collaborate with data engineers to ensure reliable ingestion from source systems
• Validate data quality, consistency, and integrity across datasets
• Partner with analytics and BI teams to ensure data readiness and usability
• Platform Operations & Governance
• Monitor and maintain warehouse performance, cost, and usage efficiency
• Implement data access controls, security policies, and governance standards
• Maintain documentation for schemas, metrics, and data definitions
• Support audit, compliance, and data lineage requirements
• Work closely with analytics, product, finance, and business stakeholders
• Provide guidance on best practices for querying and modeling
• Contribute to architectural discussions and platform evolution
• Mentor junior database or data engineers when applicable

• Bachelor’s degree in Computer Science, Engineering, Information Systems, or related field (or equivalent experience)
• 5+ years of experience as a Database Engineer, Data Engineer, or Data Warehouse Engineer
• Expert‑level SQL skills with experience handling large datasets
• Strong experience designing and operating data warehouses for analytics and BI
• Cloud Data Warehouses:
  Snowflake, Amazon Redshift, Google Big Query, Azure Synapse
• Strong knowledge of data modeling methodologies (Kimball, Inmon)
• Experience with ELT/ETL tools and orchestration frameworks
• Understanding of data partitioning, compression, and workload management
• Familiarity with BI tools (Tableau, Power BI, Looker, etc.)
• Experience with cloud infrastructure, cost optimization, and scaling strategies
• Exposure to metadata management, data catalogs, and governance tools