Sr Data Engineer

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.

• Enterprise data leadership:
  Help define and mature data integration, data consolidation, MDM integration, and data platform design patterns across Integrichain.
• Hands-on Snowflake engineering:
  Design, build, optimize, and operate Snowflake data models, pipelines, stored procedures, and high-volume data processing patterns.
• MDM/Reltio enablement:
  Partner with MDM and Product teams to support HCO Master data ingestion, outbound extracts, cross-reference data, golden record consumption, survivorship outputs, and downstream publishing patterns.
• Cross-functional partnership:
  Work with Product, Engineering, MDM, Data Science, Dev Ops, Security, and business stakeholders to align data solutions to enterprise priorities.
• Modern ELT execution:
  Use dbt or similar ELT tooling to develop reliable, maintainable, testable, and observable data pipelines.
• Cost and performance ownership:
  Drive Snowflake performance tuning, warehouse sizing, workload management, cost tracking, and cost optimization practices.

• Partner with Data Science leadership to rationalize and consolidate the enterprise data landscape across products, platforms, and acquired capabilities.
• Define reusable data integration patterns for batch, micro-batch, near-real-time, and application-to-application data exchange.
• Collaborate with cross-functional teams to understand business data needs, source-system realities, and enterprise application integration requirements.
• Design scalable patterns for ingesting, transforming, mastering, and publishing data across operational and analytical use cases.
• Help establish standards for data contracts, schema evolution, data quality, lineage, and data ownership.

• Design and build data pipelines that load source data into Reltio MDM and extract mastered outputs from Reltio for downstream Snowflake, analytics, AI, and operational use cases.
• Partner with MDM configuration and Product Management teams to translate HCO mastering requirements into data pipeline, mapping, validation, reconciliation, and publishing patterns.
• Work with Reltio APIs, exports, crosswalks/XREFs, event-based integration patterns, and bulk load/extract mechanisms as needed to support inbound and outbound data flows.
• Engineer integration patterns for HCO Master data, including party/entity, address, identifier, hierarchy, relationship, match/merge, survivorship, and golden record outputs.
• Support source ingestion and reference data integration involving datasets such as HIN, DEA, NPI, NCPDP, 340B/PHS, channel outlet data, customer/account data, and other life sciences master/reference sources.
• Develop validation and reconciliation processes to compare source data, Reltio mastered data, Snowflake curated data, and downstream consumption layers.
• Help operationalize MDM outputs for business-facing data products, semantic models, reporting tables, APIs, and AI-ready datasets.