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.
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
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
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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.