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, 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