Data Engineer

What you'll do

• Design and maintain enterprise data models supporting SaaS applications and reporting platforms
• Develop logical and physical database architectures
• Design normalized and denormalized schemas based on performance requirements
• Define data standards, naming conventions, and governance frameworks
• Design scalable, multi-tenant data structures
• Develop complex SQL queries, views, stored procedures, and functions
• Analyze query execution plans and implement performance tuning strategies
• Design indexing strategies to optimize data access and performance
• Resolve database bottlenecks, locking, and contention issues
• Implement partitioning and archival strategies for large datasets
• Monitor and tune database performance in production environments
• Design and implement CDC frameworks for near real-time data synchronization
• Build incremental data processing pipelines to optimize performance
• Track inserts, updates, and deletes across source systems
• Implement reconciliation processes to ensure data consistency
• Maintain data lineage and change tracking across systems
• Support event-driven and near real-time data architectures
• Design and develop scalable data ingestion pipelines
• Build automated workflows for integrating APIs, databases, files, and SaaS platforms
• Transform and standardize data across heterogeneous systems
• Implement error handling, retry mechanisms, monitoring, and alerting
• Optimize batch and near real-time processing workflows
• Design ingestion frameworks for REST APIs and external data providers
• Build automated synchronization processes across operational systems
• Manage schema evolution and versioning from external APIs
• Implement data validation and quality assurance controls
• Support bidirectional system integrations where required
• Implement automated data validation and quality frameworks
• Design exception reporting and reconciliation processes
• Establish data quality metrics and stewardship practices
• Monitor completeness, accuracy, and timeliness of datasets
• Support audit, compliance, and regulatory requirements
• Build backend datasets for Tableau, Power BI, Retool, and custom applications
• Design semantic data layers and reporting models
• Optimize reporting queries for large-scale datasets
• Enable self-service analytics and dashboard capabilities
• Develop reusable data services and APIs for downstream consumers
• Manage production data pipelines and database environments
• Support backup, recovery, and disaster recovery processes
• Monitor pipeline performance, data latency, and system health
• Troubleshoot production data issues and integration failures
• Coordinate schema migrations and data releases across environments

Requirements

• Strong experience in SQL and database engineering
• Hands-on expertise with ETL/ELT and data pipeline development
• Good understanding of data modeling and architecture principles
• Experience integrating data from APIs, SaaS platforms, and enterprise systems
• Familiarity with performance tuning and large-scale data systems
• Strong analytical and problem-solving skills
• Ability to work in a highly collaborative, cross-functional environment
• Experience with cloud data platforms (Azure preferred)
• Exposure to CDC, real-time streaming, and event-driven architectures
• Familiarity with data observability and monitoring tools
• Experience building data platforms supporting AI/ML use cases
• Knowledge of data governance and compliance frameworks
• Experience with analytics tools (Power BI, Tableau, etc.)

Tech stack