{"id":886473,"url":"https://alion.io/job/integrichain-senior-database-engineer-platform-engineering","title":"Senior Database Engineer - Platform Engineering","company":{"id":691777,"name":"IntegriChain Incorporated","domain":"integrichain.com","url":"https://alion.io/company/integrichain","size_band":"11-50","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"SmartRecruiters","truth_index":{"grade":"B","score":83,"open_postings":3,"ghost_share":0,"stale_share":0.667,"repost_share":0,"time_to_fill_p50_days":42,"computed_at":"2026-10-17T05:46:00Z"}},"role":"DevOps","role_family":"DevOps","seniority":"senior","employment_type":"full_time","work_mode":"hybrid","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Pune, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":17000,"max_usd":36000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":43},"experience_years_min":7,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Airflow","optional":false},{"name":"Amazon Aurora","optional":false},{"name":"Amazon DocumentDB","optional":false},{"name":"Amazon EventBridge","optional":false},{"name":"Amazon Kinesis","optional":false},{"name":"Amazon Redshift","optional":false},{"name":"Amazon S3","optional":false},{"name":"Amazon SageMaker","optional":false},{"name":"Anomaly Detection","optional":false},{"name":"Apache Iceberg","optional":false},{"name":"Apache Kafka","optional":false},{"name":"AWS","optional":false},{"name":"AWS Bedrock","optional":false},{"name":"AWS CDK","optional":false},{"name":"AWS Step Functions","optional":false},{"name":"Azure","optional":false},{"name":"Azure Data Factory","optional":false},{"name":"Azure SQL Database","optional":false},{"name":"Boto3","optional":false},{"name":"CI/CD","optional":false},{"name":"dbt","optional":false},{"name":"Delta Lake","optional":false},{"name":"DynamoDB","optional":false},{"name":"ElasticSearch","optional":false},{"name":"ETL/ELT","optional":false},{"name":"Feature Store","optional":false},{"name":"Flyway","optional":false},{"name":"GDPR","optional":false},{"name":"GitHub Actions","optional":false},{"name":"HIPAA","optional":false},{"name":"IAM","optional":false},{"name":"Least Privilege","optional":false},{"name":"Liquibase","optional":false},{"name":"LLM","optional":false},{"name":"Machine Learning","optional":false},{"name":"Memcached","optional":false},{"name":"MySQL","optional":false},{"name":"OpenSearch","optional":false},{"name":"Oracle","optional":false},{"name":"pgvector","optional":false},{"name":"Platform Engineering","optional":false},{"name":"PostgreSQL","optional":false},{"name":"Python","optional":false},{"name":"RAG","optional":false},{"name":"Redis","optional":false},{"name":"Snowflake","optional":false},{"name":"SOC 2","optional":false},{"name":"SQL","optional":false},{"name":"Terraform","optional":false},{"name":"Amazon Neptune","optional":true},{"name":"Apache Hudi","optional":true},{"name":"AWS Glue","optional":true},{"name":"Databricks","optional":true},{"name":"Great Expectations","optional":true},{"name":"Java","optional":true},{"name":"MLFlow","optional":true},{"name":"MS SQL","optional":true},{"name":"SQLAlchemy","optional":true},{"name":"Time Series Forecasting","optional":true}],"status":"live","first_seen_at":"2026-03-31T15:58:19Z","employer_posted_date":"2026-03-31","last_verified_at":"2026-10-17T07:24:53Z","board_verified":true,"closed_at":null,"days_open":199,"trust":{"level":"stale","repost_count":0,"flags":["stale"],"days_open":199},"description":"IntegriChain is the data and application backbone for market access departments of Life Sciences manufacturers. We deliver the data, the applications, and the business process infrastructure for patient access and therapy commercialization. More than 250 manufacturers rely on our ICyte Platform to orchestrate their commercial and government payer contracting, patient services, and distribution channels. ICyte is the first and only platform that unites the financial, operational, and commercial data sets required to support therapy access in the era of specialty and precision medicine. With ICyte, Life Sciences innovators can digitalize their market access operations, freeing up resources to focus on more data-driven decision support. With ICyte, Life Sciences innovators are digitalizing labor-intensive processes - freeing up their best talent to identify and resolve coverage and availability hurdles and to manage pricing and forecasting complexity.\nWe are headquartered in Philadelphia, PA (USA), with offices in: Ambler, PA (USA); Pune, India; and Medellín, Colombia. For more information, visitwww.integrichain.com, or follow us on Twitter @IntegriChain andLinkedIn.\n 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.\nYou'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.\nKey Responsibilities\nMulti-Engine Cloud Data Architecture & Platform Engineering\nDesign, 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.\nArchitect 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.\nImplement and manage Medallion Architecture (Bronze / Silver / Gold) patterns to support raw ingestion, curated analytics, and business-ready datasets.\nBuild and optimize hybrid data platforms spanning operational databases (PostgreSQL / RDS / Aurora / DynamoDB) and analytical systems (Snowflake / Redshift).\nDevelop and maintain semantic layers and analytics models to enable consistent, reusable metrics across BI, analytics, and AI use cases.\nEngineer efficient data models, ETL/ELT pipelines, and query performance tuning for analytical and transactional workloads.\nEngineer replication topologies, partitioning strategies, and data lifecycle automation as code - not manual DBA operations.\nBuild automated schema migration pipelines (Flyway/Liquibase) and data versioning workflows integrated into CI/CD replacing manual schema change management.\nDesign and implement API-first data access patterns, enabling engineering teams to interact with databases through well-defined, versioned interfaces rather than direct connection strings.\nAdvanced Data Pipelines, Streaming & Orchestration\nEngineer 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.\nBuild streaming data pipelines using AWS Kinesis Data Streams, Kinesis Firehose, and MSK (Managed Kafka) for event-driven, low-latency ingestion across multiple database targets.\nImplement data quality checks, schema enforcement, lineage, and observability across pipelines.\nOptimize performance, cost, and scalability across ingestion, transformation, and consumption layers.\nImplement change data capture (CDC) using AWS DMS, Debezium, or native engine features to synchronize data across SQL, NoSQL, and analytical systems.\nNoSQL & Document Store Engineering\nDesign and optimize DynamoDB schemas using single-table design patterns, GSIs, LSIs, and DynamoDB Streams for event-driven architectures.\nArchitect DocumentDB (MongoDB-compatible) clusters for document workloads requiring flexible schema and hierarchical data models.\nBuild and manage OpenSearch / ElasticSearch clusters for full-text search, log analytics, and observability use cases.\nEvaluate and recommend the right NoSQL engine (DynamoDB vs DocumentDB vs OpenSearch vs ElastiCache) based on access patterns, latency, and cost profile.\nImplement TTL policies, DynamoDB Accelerator (DAX), and ElastiCache (Redis/Memcached) for high-throughput caching layers.\nAI-Enabled Data Engineering & MLOps Foundations\nApply 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.\nDesign and build ML-ready data foundations: SageMaker Feature Store, training dataset pipelines, experiment tracking, and inference data pipelines using AWS-native MLOps services.\nIntegrate LLM capabilities via Amazon Bedrock for AI-assisted data documentation, query generation, lineage summarization, and automated data cataloging.\nImplement vector database solutions (pgvector on Aurora/RDS, OpenSearch k-NN) to support AI similarity search and retrieval-augmented generation (RAG) use cases.\nBuild AI-powered observability using ML-driven anomaly detection on pipeline metrics, query performance trends, and data quality SLAs.\nSoftware Engineering, DevOps & Infrastructure as Code\nBuild 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.\nIntegrate database changes into CI/CD pipelines (GitHub Actions, AWS CodePipeline) with automated schema testing, data contract validation, deployment, and rollback.\nDevelop internal platform tooling using Python, SQL, and AWS SDK (boto3) - building self-service capabilities that allow engineers to provision governed database environments on demand.\nImplement database-as-code practices: automated schema migrations, snapshot/restore testing pipelines, and environment clone automation - eliminating manual DBA provisioning tasks.\nBuild and publish internal data platform APIs and SDKs that abstract database complexity from application teams.\nSecurity, Governance & Compliance Engineering\nEngineer 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.\nDefine and enforce data contracts and ownership using AWS Lake Formation, Glue Data Catalog, and Snowflake governance - versioned and managed in source control.\nPartner with Security and Compliance teams to ensure audit readiness and regulatory alignment (SOC 2, HIPAA, GDPR where applicable).\nManage AWS IAM policies, KMS encryption, VPC security groups, and private endpoints (PrivateLink, VPC Endpoints) for least-privilege access and network isolation.\nImplement secrets management using AWS Secrets Manager and Parameter Store with automated credential rotation for all database engines.\n Experience\n7+ years of experience in database platform engineering, data engineering, or cloud infrastructure engineering in production environments.\nProven 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.\nStrong track record of designing and operating data platforms at scale in AWS environments, with databases managed as code from day one.\nAWS & Cloud Databases\nDeep hands-on expertise with AWS RDS (PostgreSQL, MySQL, Oracle), Aurora (Serverless v2, Global Database), and RDS Proxy.\nProduction experience with DynamoDB: single-table design, GSI/LSI strategy, Streams, DAX, and capacity planning.\nWorking knowledge of AWS Redshift, Glue, Lake Formation, Kinesis, MSK, and EventBridge for pipeline and lakehouse architectures.\nFamiliarity with Azure SQL, Azure Data Factory, or Azure Synapse is a plus.\nSnowflake\nStrong 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.\nSQL, NoSQL & Data Modeling\nDeep SQL expertise across multiple engines (PostgreSQL, T-SQL, Snowflake SQL, DynamoDB PartiQL).\nStrong understanding of Medallion Architecture, semantic layers, and analytics engineering best practices.\nProven NoSQL data modeling: DynamoDB single-table design, document store schema design, and search index architecture.\nPipelines & Orchestration\nExperience building and operating advanced ELT/ETL pipelines using dbt, AWS Glue, Airflow, or similar orchestration frameworks.\nHands-on experience with streaming ingestion using Kinesis, MSK (Kafka), or equivalent event-driven technologies.\nFamiliarity with CDC patterns and tools (DMS, Debezium) for cross-engine data synchronization.\nAI & ML Data Foundations\nUnderstanding of ML pipeline requirements: feature engineering, training dataset preparation, model versioning, and inference data patterns.\nExposure to AWS SageMaker, Bedrock, or equivalent ML platforms from a data infrastructure perspective.\nAwareness of vector databases and embedding-based retrieval (pgvector, OpenSearch k-NN) is a strong plus.\nInfrastructure & Automation\nProficiency with Terraform for database and cloud infrastructure as code; AWS CDK experience is a plus.\nProficiency with Python (boto3, SQLAlchemy, pandas) and SQL for data transformation, automation, and tooling.\nExperience integrating database workflows into CI/CD pipelines using GitHub Actions, CodePipeline, or similar.\nWhat Success Looks Like\nWithin the First 90 Days\nFully onboarded and delivering enhancements across Snowflake, RDS, Aurora, and DynamoDB environments.\nConducted a comprehensive audit of existing database architectures and delivered a prioritized improvement roadmap.\nDelivering optimized queries, schemas, and automation for key systems.\nEstablished IaC coverage for at least one previously manually-provisioned database environment.\nOngoing Outcomes\nMeasurable improvements in query performance, pipeline reliability, and data platform scalability across all database engines.\nZero manual database provisioning - all environments managed through infrastructure as code and CI/CD pipelines.\nContinuous collaboration across teams to enhance data availability and governance.\nAI-powered automation reducing manual operational overhead in database monitoring, anomaly detection, and data quality management.\nML-ready data foundations enabling Data Science teams to ship faster with governed, reproducible datasets.\nBonus Experience (Nice to Have)\nAWS certifications: AWS Database Specialty, AWS Solutions Architect, AWS Data Engineer Associate.\nSnowflake SnowPro Core or Advanced Data Engineer certification.\nExperience with Apache Iceberg, Delta Lake, or Hudi for open table format lakehouse architectures.\nHands-on experience with SageMaker Feature Store, Model Registry, or MLflow for MLOps workflows.\nFamiliarity with data observability platforms (Monte Carlo, Bigeye) or custom observability with Great Expectations / dbt tests.\nExperience with graph databases (Neptune) or time-series databases (Timestream) in AWS.\nExposure to Databricks on AWS or Azure for unified data and AI workloads\n What does IntegriChain have to offer?\nMission driven: Work...","description_format":"text","description_chars":12895,"description_truncated":true,"requirements":{"experience_years_min":7,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":null,"security_clearance":false,"languages":[{"language":"English","level":"All levels","optional":false}]},"benefits":["Parental leave"],"hiring_locations":[{"name":"India","iso":"IN","kind":"country"}],"hiring_excludes":[],"relocation_offered":false,"industries":["Biotechnology","Health Data & Interoperability","Healthcare & Life Sciences Consulting"],"lifecycle":[{"event":"open","at":"2026-09-14T04:11:40Z"}],"visa":[],"liveness":{"score":7,"band":"cold","label":"Long shot","p_open":1,"p_active":0.261,"p_room":0.28,"age_days":199,"expected_fill_days":42,"reasons":["conf:2","velocity","win:tail","crowd:"],"computed_at":"2026-10-17T05:46:00Z"},"pay":null,"html_url":"https://alion.io/job/integrichain-senior-database-engineer-platform-engineering","json_url":"https://alion.io/job/integrichain-senior-database-engineer-platform-engineering.json","meta":{"generated_at":"2026-10-17T07:41:37Z","cache_seconds":300,"methodology":"https://alion.io/methodology","terms":"https://alion.io/terms","contact":"https://alion.io/contact","api":"https://alion.io/developers","about":"Alion is a live layer of people, companies and AI agents: who they are, whether they are real and active right now, what they do and how to work with them, readable by people and by agents and paid per call.","catalog":"https://alion.io/catalog.json","usage":{"tier":"crawler_verified","counted_by":"address","units_charged":1,"used_today":10359,"day_limit":null,"remaining_today":null,"minute_limit":300,"resets_at":"2026-10-18T00:00:00Z"}},"offers":[{"id":"company.slices","title":"One company in depth, by slice","status":"live","price":{"credits":0.02,"usd":0.002,"plus_per_slice":{"credits":0.05,"usd":0.005}},"unit":"per company, plus each slice with data","note":"the employer in depth","call":{"mcp_tool":"get_company","arguments":{"id":691777},"rest":"https://alion.io/mcp/rest/get_company?id=691777"},"human":"https://alion.io/catalog?offer=company.slices&for=job%2Fintegrichain-senior-database-engineer-platform-engineering"},{"id":"market.stats","title":"A market slice: pay, demand and time to fill","status":"live","price":{"credits":1,"usd":0.1},"unit":"per slice","note":"pay, demand and time to fill for this role and place","call":{"mcp_tool":"market_stats"},"human":"https://alion.io/catalog?offer=market.stats&for=job%2Fintegrichain-senior-database-engineer-platform-engineering"},{"id":"job.search","title":"Open jobs by role, technology, place, pay and visa","status":"live","price":{"credits":0.02,"usd":0.002},"unit":"per posting in a list","note":"similar open postings","call":{"mcp_tool":"search_jobs"},"human":"https://alion.io/catalog?offer=job.search&for=job%2Fintegrichain-senior-database-engineer-platform-engineering"},{"id":"company.verify","title":"Is this company real and active right now","status":"pilot","price":null,"unit":"per company","request":{"url":"https://alion.io/catalog/request","method":"POST","body":"{\"offer\": \"company.verify\", \"for\": \"job/integrichain-senior-database-engineer-platform-engineering\", \"note\": \"what you need it for\"}"},"human":"https://alion.io/catalog?offer=company.verify&for=job%2Fintegrichain-senior-database-engineer-platform-engineering"}]}