Senior Database Engineer

​​​​​​Join our DevOps Engineering team as a Senior Database Engineer responsible for designing, optimizing, and automating cloud database solutions across AWS RDS, Postgres, and Snowflake. This role focuses on performance engineering, data integration, and automation ensuring our data platforms are scalable, reliable, and efficient. You’ll work closely with DevOps and Product Engineering to build high-performing data infrastructure that supports critical applications and analytics.Key Responsibilities: Modern Data Architecture & Platform EngineeringDesign, build, and optimize database solutions using Snowflake, PostgreSQL, and Oracle RDS.Design and evolve cloud-native data lakehouse architectures using Snowflake, AWS, and open data formats where appropriate.Implement and manage Medallion Architecture (Bronze / Silver / Gold) patterns to support raw ingestion, curated analytics, and business-ready datasets.Build and optimize hybrid data platforms spanning operational databases (PostgreSQL / RDS) and analytical systems (Snowflake).Develop and maintain semantic layers and analytics models to enable consistent, reusable metrics across BI, analytics, and AI use cases.Engineer efficient data models, ETL/ELT pipelines, and query performance tuning for analytical and transactional workloads.Implement replication, partitioning, and data lifecycle management to enhance scalability and resilience.Manage schema evolution, data versioning, and change management in multienvironment deployments Advanced Data Pipelines & OrchestrationEngineer highly reliable ELT pipelines using modern tooling (e.g., dbt, cloud-native services, event-driven ingestion).Design pipelines that support batch, micro-batch, and near–real-time processing.Implement data quality checks, schema enforcement, lineage, and observability across pipelines.Optimize performance, cost, and scalability across ingestion, transformation, and consumption layers.AI-Enabled Data EngineeringApply AI and ML techniques to data architecture and operations, including:Intelligent data quality validation and anomaly detectionAutomated schema drift detection and impact analysisQuery optimization and workload pattern analysisDesign data foundations that support ML feature stores, training datasets, and inference pipelines.Collaborate with Data Science teams to ensure data platforms are AI-ready, reproducible, and governed.Automation, DevOps & Infrastructure as CodeBuild and manage data infrastructure as code using Terraform and cloud-native services.Integrate data platforms into CI/CD pipelines, enabling automated testing, deployment, and rollback of data changes.Develop tooling and automation (Python, SQL, APIs) to streamline provisioning, monitoring, and operational workflows.Security, Governance & ComplianceImplement enterprise-grade data governance, including role-based access control, encryption, masking, and auditing.Enforce data contracts, ownership, and lifecycle management across the lakehouse.Partner with Security and Compliance teams to ensure audit readiness and regulatory alignment.Build and manage data infrastructure as code using Terraform and cloud-native services.Integrate data platforms into CI/CD pipelines, enabling automated testing, deployment, and rollback of data changes.Develop tooling and automation (Python, SQL, APIs) to streamline provisioning, monitoring, and operational workflows.