Sr Data Engineer

Sr Data Engineer

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.

We are headquartered in Philadelphia, PA (USA), with offices in: Ambler, PA (USA); Pune, India; and Medellín, Colombia.

Job Description

Position Overview

• 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, DevOps, 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.

Key Responsibilities

Data Strategy, Consolidation, and Integration

• 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.

MDM / Reltio Data Engineering Enablement

• 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.

Snowflake Platform Engineering and Optimization

• Design Snowflake database, schema, table, view, and semantic-layer patterns that support performance, governance, and maintainability.
• Optimize Snowflake workloads using clustering, micro-partition awareness, warehouse sizing, query profiling, caching behavior, and workload isolation.
• Implement Snowflake cost tracking and optimization practices, including warehouse utilization monitoring, inefficient query identification, and cost allocation by workload, team, or use case.
• Build scalable SQL and Snowflake stored procedure logic for large-volume data processing and analytical workloads.
• Apply secure Snowflake design patterns including RBAC, masking, access isolation, auditing, and environment separation.

ETL/ELT, dbt, Python, and Data Pipeline Development

• Design, build, and maintain reliable ELT pipelines using dbt or comparable modern data transformation tooling.
• Develop Python-based automation for API integration, file processing, metadata management, validation, orchestration support, and operational tooling.
• Develop modular, tested, and reusable transformation models for raw, curated, mastered, and business-ready data layers.
• Implement automated data quality checks, source freshness checks, reconciliation, logging, and exception-handling patterns.
• Build orchestration-ready pipelines that support dependency management, restartability, incremental loads, and operational monitoring.
• Collaborate with DevOps/SRE teams on CI/CD, deployment automation, environment promotion, and operational runbooks for data pipelines.

Data Modeling and Big Data Processing

• Spearhead logical and physical data modeling efforts for enterprise analytical, operational, MDM, and AI-ready datasets.
• Design models that balance normalization, dimensional modeling, medallion/lakehouse concepts, and application-specific consumption needs.
• Create denormalized reporting and semantic-model-ready structures that simplify business consumption and reduce ambiguity for AI/LLM use cases.
• Process and optimize large data volumes in Snowflake using efficient SQL, PL/SQL-style procedural logic, Snowflake Scripting, and performance-aware design.
• Create reusable patterns for historical tracking, snapshots, audit columns, data versioning, and lifecycle management.
• Ensure data models support downstream BI, AI/ML, semantic models, data apps, MDM Explorer/Entity 360 use cases, and enterprise reporting.

Qualifications

Required Skills and Experience

• 10+ years of experience in data engineering, database engineering, analytics engineering, or data platform development in production environments.
• Strong hands-on experience with Snowflake, including architecture, performance tuning, security design, cost optimization, and cost tracking.
• Thorough understanding of Snowflake design patterns for analytical workloads, high-volume data processing, data sharing, and multi-environment deployments.
• Hands-on experience with ETL/ELT tools; dbt experience is strongly preferred.
• Strong SQL and PL/SQL-style development experience, including complex transformations, stored procedures, performance tuning, and large-scale data processing.
• Python experience for data automation, API integration, file handling, data validation, metadata processing, or operational tooling.
• Experience designing and implementing enterprise data models, curated data layers, semantic layers, and reusable data products.
• Experience with data integration patterns across enterprise applications, APIs, files, cloud storage, operational systems, MDM platforms, and analytical platforms.
• Working understanding of Master Data Management concepts such as golden records, crosswalks/XREFs, match/merge, survivorship, hierarchies, entity relationships, stewardship, and data quality.
• Experience partnering with MDM, Product, or business teams to translate mastering requirements into source-to-target mappings, transformation logic, validations, and downstream data consumption patterns.
• Ability to work directly with cross-functional stakeholders to gather requirements, explain design tradeoffs, and drive alignment.
• Experience implementing data quality, lineage, auditability, observability, and operational monitoring within data pipelines.
• Comfortable operating as a hands-on senior individual contributor who can also influence strategy and engineering standards.

Preferred Experience

• Experience with Reltio MDM, including inbound data loads, outbound exports, Reltio APIs, crosswalks, match/merge outputs, survivorship outputs, and operational troubleshooting.
• Experience in life sciences, healthcare, pharma commercialization, HCO/HCP mastering, patient data, channel data, customer master, or commercial data platforms.
• Experience with life sciences reference and