Senior Data Engineer

This is a fully remote position, open to applicants in Pennsylvania.

📋 Description

• Assist in defining and enhancing data integration, data consolidation, MDM integration, and data platform design patterns across Integrichain.

• Design, create, optimize, and manage Snowflake data models, pipelines, stored procedures, and high-volume data processing methodologies.

• Collaborate with MDM and Product teams to facilitate HCO Master data ingestion, outbound extracts, cross-reference data, golden record consumption, survivorship outputs, and downstream publishing strategies.

• Work alongside Product, Engineering, MDM, Data Science, DevOps, Security, and business stakeholders to align data solutions with enterprise priorities.

• Utilize dbt or similar ELT tools to create reliable, maintainable, testable, and observable data pipelines.

• Lead Snowflake performance tuning, warehouse sizing, workload management, cost tracking, and cost optimization initiatives.

• Collaborate with Data Science leadership to rationalize and consolidate the enterprise data landscape across products, platforms, and acquired capabilities.

• Establish reusable data integration patterns for batch, micro-batch, near-real-time, and application-to-application data exchange.

• Work with cross-functional teams to comprehend business data requirements, source-system realities, and enterprise application integration needs.

• Develop scalable patterns for ingesting, transforming, mastering, and publishing data across operational and analytical use cases.

• Assist in setting standards for data contracts, schema evolution, data quality, lineage, and data ownership.

• Create and manage data pipelines that load source data into Reltio MDM and extract mastered outputs from Reltio for downstream Snowflake, analytics, AI, and operational applications.

• Collaborate with MDM configuration and Product Management teams to translate HCO mastering requirements into data pipeline, mapping, validation, reconciliation, and publishing patterns.

• Engage with Reltio APIs, exports, crosswalks/XREFs, event-based integration patterns, and bulk load/extract mechanisms as necessary 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.

• Assist in operationalizing 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 uphold 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.

• Develop 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 dependable ELT pipelines using dbt or comparable modern data transformation tools.

• Create 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.

• Construct orchestration-ready pipelines that support dependency management, restartability, incremental loads, and operational monitoring.

• Work with DevOps/SRE teams on CI/CD, deployment automation, environment promotion, and operational runbooks for data pipelines.

• Lead 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-conscious design.

• Establish reusable patterns for historical tracking, snapshots, audit columns, data versioning, and lifecycle management.

• Ensure data models facilitate downstream BI, AI/ML, semantic models, data applications, MDM Explorer/Entity 360 use cases, and enterprise reporting.

⛳️ Requirements

• Over 10 years of experience in data engineering, database engineering, analytics engineering, or data platform development in production settings.

• Extensive hands-on experience with Snowflake, encompassing architecture, performance tuning, security design, cost optimization, and cost tracking.

• Comprehensive understanding of Snowflake design patterns for analytical workloads, high-volume data processing, data sharing, and multi-environment deployments.

• Practical experience with ETL/ELT tools; proficiency in dbt is highly preferred.

• Strong SQL and PL/SQL-style development skills, including complex transformations, stored procedures, performance tuning, and large-scale data processing.

• Experience with Python for data automation, API integration, file handling, data validation, metadata processing, or operational tooling.

• Proven experience in designing and implementing enterprise data models, curated data layers, semantic layers, and reusable data products.

• Familiarity with data integration patterns across enterprise applications, APIs, files, cloud storage, operational systems, MDM platforms, and analytical platforms.

• Working knowledge of Master Data Management concepts such as golden records, crosswalks/XREFs, match/merge, survivorship, hierarchies, entity relationships, stewardship, and data quality.

• Experience collaborating 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 directly engage with cross-functional stakeholders to gather requirements, clarify design trade-offs, and drive alignment.

• Experience implementing data quality, lineage, auditability, observability, and operational monitoring within data pipelines.

• Comfortable functioning as a hands-on senior individual contributor who can also influence strategy and engineering standards.

🏝️ Benefits

• Excellent and affordable medical benefits

• Flexible Paid Time Off

• Robust Learning & Development opportunities including over 700+ development courses free to all employees