Join our DevOps Engineering team as a Senior Database Engineer to design, build, and engineer cloud-native database platforms across a modern, multi-engine data stack. This is an engineering role, not a DBA role, focused on building scalable systems, writing infrastructure-as-code, and embedding databases into software delivery pipelines. You'll work closely with DevOps and Product Engineering to build high-performing data infrastructure that supports critical applications and analytics. You will own and evolve a diverse ecosystem spanning AWS RDS, Aurora, DynamoDB, Redshift, Azure SQL, PostgreSQL, Snowflake, and NoSQL engines, integrating AI-driven automation and MLOps-ready data foundations to support critical applications and machine learning workflows. Key Responsibilities Multi-Engine Cloud Data Architecture & Platform Engineering - Design, build, and engineer hybrid data solutions spanning relational (PostgreSQL, Aurora, RDS, Azure SQL), columnar (Redshift, Snowflake), and NoSQL (DynamoDB, DocumentDB, OpenSearch) engines - selecting the right engine per workload. - Architect cloud-native data lakehouse platforms on AWS using S3, Lake Formation, Glue, and open formats (Apache Iceberg, Delta Lake, Parquet), with Azure Data Lake as a secondary target. - 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 / Aurora / DynamoDB) and analytical systems (Snowflake / Redshift). - 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. - Engineer replication topologies, partitioning strategies, and data lifecycle automation as code - not manual DBA operations. - Build automated schema migration pipelines (Flyway/Liquibase) and data versioning workflows integrated into CI/CD replacing manual schema change management. - Design and implement API-first data access patterns, enabling engineering teams to interact with databases through well-defined, versioned interfaces rather than direct connection strings. Advanced Data Pipelines, Streaming & Orchestration - Engineer ELT/ETL pipelines using AWS-native services (Glue, Kinesis, MSK, Step Functions, EventBridge) and modern tooling (dbt, Airflow) for batch, micro-batch, and near-real-time workloads. - Build streaming data pipelines using AWS Kinesis Data Streams, Kinesis Firehose, and MSK (Managed Kafka) for event-driven, low-latency ingestion across multiple database targets. - Implement data quality checks, schema enforcement, lineage, and observability across pipelines. - Optimize performance, cost, and scalability across ingestion, transformation, and consumption layers. - Implement change data capture (CDC) using AWS DMS, Debezium, or native engine features to synchronize data across SQL, NoSQL, and analytical systems. NoSQL & Document Store Engineering - Design and optimize DynamoDB schemas using single-table design patterns, GSIs, LSIs, and DynamoDB Streams for event-driven architectures. - Architect DocumentDB (MongoDB-compatible) clusters for document workloads requiring flexible schema and hierarchical data models. - Build and manage OpenSearch / ElasticSearch clusters for full-text search, log analytics, and observability use cases. - Evaluate and recommend the right NoSQL engine (DynamoDB vs DocumentDB vs OpenSearch vs ElastiCache) based on access patterns, latency, and cost profile. - Implement TTL policies, DynamoDB Accelerator (DAX), and ElastiCache (Redis/Memcached) for high-throughput caching layers. AI-Enabled Data Engineering & MLOps Foundations - Apply AI and ML techniques to data architecture and operations, including intelligent data quality validation, anomaly detection, schema drift detection, and query workload pattern analysis - using AWS SageMaker and Amazon Bedrock. - Design and build ML-ready data foundations: SageMaker Feature Store, training dataset pipelines, experiment tracking, and inference data pipelines using AWS-native MLOps services. - Integrate LLM capabilities via Amazon Bedrock for AI-assisted data documentation, query generation, lineage summarization, and automated data cataloging. - Implement vector database solutions (pgvector on Aurora/RDS, OpenSearch k-NN) to support AI similarity search and retrieval-augmented generation (RAG) use cases. - Build AI-powered observability using ML-driven anomaly detection on pipeline metrics, query performance trends, and data quality SLAs. Software Engineering, DevOps & Infrastructure as Code - Build and manage all data infrastructure as code using Terraform and AWS CDK - covering RDS, Aurora, DynamoDB, Redshift, Glue, MSK, Kinesis, Snowflake, and supporting IAM/networking components. - Integrate database changes into CI/CD pipelines (GitHub Actions, AWS CodePipeline) with automated schema testing, data contract validation, deployment, and rollback. - Develop internal platform tooling using Python, SQL, and AWS SDK (boto3) - building self-service capabilities that allow engineers to provision governed database environments on demand. - Implement database-as-code practices: automated schema migrations, snapshot/restore testing pipelines, and environment clone automation - eliminating manual DBA provisioning tasks. - Build and publish internal data platform APIs and SDKs that abstract database complexity from application teams. Security, Governance & Compliance Engineering - Engineer enterprise-grade data governance across all engines: RBAC, column/row-level security, field-level encryption, dynamic data masking, and comprehensive audit logging, implemented as code, not manual configuration. - Define and enforce data contracts and ownership using AWS Lake Formation, Glue Data Catalog, and Snowflake governance - versioned and managed in source control. - Partner with Security and Compliance teams to ensure audit readiness and regulatory alignment (SOC 2, HIPAA, GDPR where applicable). - Manage AWS IAM policies, KMS encryption, VPC security groups, and private endpoints (PrivateLink, VPC Endpoints) for least-privilege access and network isolation. - Implement secrets management using AWS Secrets Manager and Parameter Store with automated credential rotation for all database engines. Qualifications Experience - 7+ years of experience in database platform engineering, data engineering, or cloud infrastructure engineering in production environments. - Proven experience as a lead or senior engineer on multi-engine database platforms spanning both SQL and NoSQL workloads - with a software engineering, not administration, mindset. - Strong track record of designing and operating data platforms at scale in AWS environments, with databases managed as code from day one. AWS & Cloud Databases - Deep hands-on expertise with AWS RDS (PostgreSQL, MySQL, Oracle), Aurora (Serverless v2, Global Database), and RDS Proxy. - Production experience with DynamoDB: single-table design, GSI/LSI strategy, Streams, DAX, and capacity planning. - Working knowledge of AWS Redshift, Glue, Lake Formation, Kinesis, MSK, and EventBridge for pipeline and lakehouse architectures. - Familiarity with Azure SQL, Azure Data Factory, or Azure Synapse is a plus. Snowflake - Strong hands-on Snowflake experience: performance tuning (clustering, materialized views, query profiling), cost optimization (warehouse sizing, auto-suspend, credits), security (RBAC, dynamic masking, network policies), and data sharing. SQL, NoSQL & Data Modeling - Deep SQL expertise across multiple engines (PostgreSQL, T-SQL, Snowflake SQL, DynamoDB PartiQL). - Strong understanding of Medallion Architecture, semantic layers, and analytics engineering best practices. - Proven NoSQL data modeling: DynamoDB single-table design, document store schema design, and search index architecture. Pipelines & Orchestration - Experience building and operating advanced ELT/ETL pipelines using dbt, AWS Glue, Airflow, or similar orchestration frameworks. - Hands-on experience with streaming ingestion using Kinesis, MSK (Kafka), or equivalent event-driven technologies. - Familiarity with CDC patterns and tools (DMS, Debezium) for cross-engine data synchronization. AI & ML Data Foundations - Understanding of ML pipeline requirements: feature engineering, training dataset preparation, model versioning, and inference data patterns. - Exposure to AWS SageMaker, Bedrock, or equivalent ML platforms from a data infrastructure perspective. - Awareness of vector databases and embedding-based retrieval (pgvector, OpenSearch k-NN) is a strong plus. Infrastructure & Automation - Proficiency with Terraform for database and cloud infrastructure as code; AWS CDK experience is a plus. - Proficiency with Python (boto3, SQLAlchemy, pandas) and SQL for data transformation, automation, and tooling. - Experience integrating database workflows into CI/CD pipelines using GitHub Actions, CodePipeline, or similar. What Success Looks Like Within the First 90 Days - Fully onboarded and delivering enhancements across Snowflake, RDS, Aurora, and DynamoDB environments. - Conducted a comprehensive audit of existing database architectures and delivered a prioritized improvement roadmap. - Delivering optimized queries, schemas, and automation for key systems. - Established IaC coverage for at least one previously manually-provisioned database environment. Ongoing Outcomes - Measurable improvements in query performance, pipeline reliability, and data platform scalability across all database engines. - Zero manual database provisioning - all environments managed through infrastructure as code and CI/CD pipelines. - Continuous collaboration across teams to enhance data availability and governance. - AI-powered automation reducing manual operational overhead in database monitoring, anomaly detection, and data quality management. - ML-ready data foundations enabling Data Science teams to ship faster with governed, reproducible datasets. Bonus Experience (Nice to Have) - AWS certifications: AWS Database Specialty, AWS Solutions Architect, AWS Data Engineer Associate. - Snowflake SnowPro Core or Advanced Data Engineer certification. - Experience with Apache Iceberg, Delta Lake, or Hudi for open table format lakehouse architectures. - Hands-on experience with SageMaker Feature Store, Model Registry, or MLflow for MLOps workflows. - Familiarity with data observability platforms (Monte Carlo, Bigeye) or custom observability with Great Expectations / dbt tests. - Experience with graph databases (Neptune) or time-series databases (Timestream) in AWS. - Exposure to Databricks on AWS or Azure for unified data and AI workloads Additional Information What does IntegriChain have to offer? - Mission driven: Work with the purpose of helping to improve patients' lives! - Excellent and affordable medical benefits + non-medical perks including Student Loan Reimbursement, Flexible Paid Time Off and Paid Parental Leave - 401(k) Plan with a Company Match to prepare for your future - Robust Learning & Development opportunities including over 700+ development courses free to all employees #LI-ZG1 Videos To Watch https://youtu.be/2Q_ODlJxDcQ?feature=shared