Senior Data Engineer

Role Overview

• Help define and mature data integration, data consolidation, MDM integration, and data platform design patterns across Integrichain.
• Design, build, optimize, and operate Snowflake data models, pipelines, stored procedures, and high-volume data processing patterns.
• 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.
• Work with Product, Engineering, MDM, Data Science, DevOps, Security, and business stakeholders to align data solutions to enterprise priorities.
• Use dbt or similar ELT tooling to develop reliable, maintainable, testable, and observable data pipelines.
• 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 DevOps/SRE teams on CI/CD, deployment automation, environment promotion, and operational runbooks for data pipelines.
• 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.

Requirements

• 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.

Tech Stack

• Cloud
• ETL
• Python
• SQL

Benefits

• Excellent and affordable medical benefits
• Flexible Paid Time Off
• Robust Learning & Development opportunities including over 700+ development courses free to all employees