Senior Database Engineer - Platform Engineering

Integri Chain 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.

We are headquartered in Philadelphia, PA (USA), with offices in:
Ambler, PA (USA);
Pune, India; and Medellín, Colombia. For more information, visit , or follow us on Twitter @Integri Chain and Linked In.

This role offers flexibility, but candidates must reside in Pennsylvania, New Jersey, or New York and be within a reasonable travel distance of our Philadelphia office, as regular in-person collaboration is required.

Join our Dev Ops 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 Dev Ops 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 No

SQL engines, integrating AI‑driven automation and MLOps‑ready data foundations to support critical applications and machine learning workflows.

• Design, build, and engineer hybrid data solutions spanning relational (PostgreSQL, Aurora, RDS, Azure SQL), columnar (Redshift, Snowflake), and No
  
  SQL (DynamoDB, Document DB, Open Search) 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.

• Engineer ELT/ETL pipelines using AWS‑native services (Glue, Kinesis, MSK, Step Functions, Event Bridge) 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,…