Sr Data Engineer

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, 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.

• 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.

• 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 Dev Ops/SRE teams on CI/CD, deployment automation,…