Senior Database Engineer

Job Description
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 Engineering

• Design, 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 & Orchestration

• Engineer 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 Engineering

• Apply AI and ML techniques to data architecture and operations, including:
• Intelligent data quality validation and anomaly detection
• Automated schema drift detection and impact analysis
• Query optimization and workload pattern analysis
• Design 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 Code

• 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.

Security, Governance & Compliance

• Implement 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.