Senior Data Engineer

Vaga não está no LinkedIn

🕒 Junho 25

🔔 Pennsylvania – Remoto

🔔 Pennsylvania – Remote

⏰ Tempo Integral

🟠 Sênior

🚰 Engenheiro de Dados

🦅 Patrocina Visto H1B

Esta empresa já patrocinou vistos H1B no passado.

👻 Score fantasma 21%

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IntegriChain

SiteLinkedInTodas as Vagas

501 - 1000 funcionários

💼 Consultoria

📦 Logística

⚕️ Seguro de Saúde

💰 Private Equity Round em 2016-03

Consulting • Logistics • Healthcare Insurance

A IntegriChain é um fornecedor líder de soluções orientadas por dados, projetadas para aprimorar a comercialização e o acesso ao mercado farmacêutico. A empresa oferece uma plataforma extensa que integra estratégia, dados, aplicativos e infraestrutura, adaptada especificamente para apoiar a comercialização de terapias. A IntegriChain fornece análises, aplicativos e serviços gerenciados que ajudam os fabricantes farmacêuticos a otimizar o alcance e a rentabilidade de seus medicamentos, aproveitando dados de canais, pacientes e pagadores. Seus serviços incluem consultoria estratégica e operacional, cobrindo aspectos chave como contratos, preços, acesso a pacientes, distribuição e logística.

Descrição

• 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.

🎯 Requisitos

• 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.

🏖️ Benefícios

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