Senior Database Engineer Platform Engineering

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.

Youll 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

IntegriChain is committed to equal treatment and opportunity in all aspects of recruitment selection and employment without regard to race color religion national origin ethnicity age sex marital status physical or mental disability gender identity sexual orientation veteran or military status or any other category protected under the law. IntegriChain is an equal opportunity employer; committed to creating a community of inclusion and an environment free from discrimination harassment and retaliation.

Our policy on visa sponsorship for US based positions: Applicants for employment in the US must have valid work authorization that does not now and/or will not in the future require sponsorship of a visa for employment authorization in the US by IntegriChain.

Remote Work :

Yes

Employment Type :

Full-time