{"id":886468,"url":"https://alion.io/job/integrichain-sr-data-engineer","title":"Sr Data Engineer","company":{"id":691777,"name":"IntegriChain Incorporated","domain":"integrichain.com","url":"https://alion.io/company/integrichain","size_band":"11-50","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"SmartRecruiters","truth_index":{"grade":"B","score":75,"open_postings":3,"ghost_share":0,"stale_share":1,"repost_share":0,"time_to_fill_p50_days":42,"computed_at":"2026-11-07T05:47:08Z"}},"role":"Data Science","role_family":"Data Science","seniority":"senior","employment_type":"full_time","work_mode":"remote","remote_scope":"stated_countries","remote_scope_basis":"board_field","remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Philadelphia, United States"],"countries":["US"],"hiring_countries":["US"],"hiring_countries_total":1,"salary":null,"salary_estimate":{"min_usd":115000,"max_usd":210000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":397},"experience_years_min":10,"visa_sponsorship":true,"relocation_package":false,"has_equity":false,"technologies":[{"name":"CI/CD","optional":false},{"name":"dbt","optional":false},{"name":"Dimensional Modeling","optional":false},{"name":"ETL/ELT","optional":false},{"name":"LLM","optional":false},{"name":"Master Data Management","optional":false},{"name":"Platform Engineering","optional":false},{"name":"Python","optional":false},{"name":"Snowflake","optional":false},{"name":"SQL","optional":false},{"name":"AWS","optional":true},{"name":"Azure","optional":true},{"name":"Dagster","optional":true},{"name":"Feature Store","optional":true},{"name":"Git","optional":true},{"name":"Terraform","optional":true}],"status":"live","first_seen_at":"2026-06-24T19:15:09Z","employer_posted_date":"2026-06-24","last_verified_at":"2026-11-07T04:30:39Z","board_verified":true,"closed_at":null,"days_open":135,"trust":{"level":"stale","repost_count":0,"flags":["stale"],"days_open":135},"description":"IntegriChain is the data and application backbone for market access departments of Life Sciences manufacturers. We deliver the data, the applications, and the business process infrastructure for patient access and therapy commercialization. More than 250 manufacturers rely on our ICyte Platform to orchestrate their commercial and government payer contracting, patient services, and distribution channels. ICyte is the first and only platform that unites the financial, operational, and commercial data sets required to support therapy access in the era of specialty and precision medicine. With ICyte, Life Sciences innovators can digitalize their market access operations, freeing up resources to focus on more data-driven decision support. With ICyte, Life Sciences innovators are digitalizing labor-intensive processes - freeing up their best talent to identify and resolve coverage and availability hurdles and to manage pricing and forecasting complexity.\nWe are headquartered in Philadelphia, PA (USA), with offices in: Ambler, PA (USA); Pune, India; and Medellín, Colombia. For more information, visitwww.integrichain.com, or follow us on Twitter @IntegriChain andLinkedIn.\n Position Overview\nEnterprise data leadership: Help define and mature data integration, data consolidation, MDM integration, and data platform design patterns across Integrichain.\nHands-on Snowflake engineering: Design, build, optimize, and operate Snowflake data models, pipelines, stored procedures, and high-volume data processing patterns.\nMDM/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.\nCross-functional partnership: Work with Product, Engineering, MDM, Data Science, DevOps, Security, and business stakeholders to align data solutions to enterprise priorities.\nModern ELT execution: Use dbt or similar ELT tooling to develop reliable, maintainable, testable, and observable data pipelines.\nCost and performance ownership: Drive Snowflake performance tuning, warehouse sizing, workload management, cost tracking, and cost optimization practices.\nKey Responsibilities\nData Strategy, Consolidation, and Integration\nPartner with Data Science leadership to rationalize and consolidate the enterprise data landscape across products, platforms, and acquired capabilities.\nDefine reusable data integration patterns for batch, micro-batch, near-real-time, and application-to-application data exchange.\nCollaborate with cross-functional teams to understand business data needs, source-system realities, and enterprise application integration requirements.\nDesign scalable patterns for ingesting, transforming, mastering, and publishing data across operational and analytical use cases.\nHelp establish standards for data contracts, schema evolution, data quality, lineage, and data ownership.\nMDM / Reltio Data Engineering Enablement\nDesign 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.\nPartner with MDM configuration and Product Management teams to translate HCO mastering requirements into data pipeline, mapping, validation, reconciliation, and publishing patterns.\nWork with Reltio APIs, exports, crosswalks/XREFs, event-based integration patterns, and bulk load/extract mechanisms as needed to support inbound and outbound data flows.\nEngineer integration patterns for HCO Master data, including party/entity, address, identifier, hierarchy, relationship, match/merge, survivorship, and golden record outputs.\nSupport 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.\nDevelop validation and reconciliation processes to compare source data, Reltio mastered data, Snowflake curated data, and downstream consumption layers.\nHelp operationalize MDM outputs for business-facing data products, semantic models, reporting tables, APIs, and AI-ready datasets.\nSnowflake Platform Engineering and Optimization\nDesign Snowflake database, schema, table, view, and semantic-layer patterns that support performance, governance, and maintainability.\nOptimize Snowflake workloads using clustering, micro-partition awareness, warehouse sizing, query profiling, caching behavior, and workload isolation.\nImplement Snowflake cost tracking and optimization practices, including warehouse utilization monitoring, inefficient query identification, and cost allocation by workload, team, or use case.\nBuild scalable SQL and Snowflake stored procedure logic for large-volume data processing and analytical workloads.\nApply secure Snowflake design patterns including RBAC, masking, access isolation, auditing, and environment separation.\nETL/ELT, dbt, Python, and Data Pipeline Development\nDesign, build, and maintain reliable ELT pipelines using dbt or comparable modern data transformation tooling.\nDevelop Python-based automation for API integration, file processing, metadata management, validation, orchestration support, and operational tooling.\nDevelop modular, tested, and reusable transformation models for raw, curated, mastered, and business-ready data layers.\nImplement automated data quality checks, source freshness checks, reconciliation, logging, and exception-handling patterns.\nBuild orchestration-ready pipelines that support dependency management, restartability, incremental loads, and operational monitoring.\nCollaborate with DevOps/SRE teams on CI/CD, deployment automation, environment promotion, and operational runbooks for data pipelines.\nData Modeling and Big Data Processing\nSpearhead logical and physical data modeling efforts for enterprise analytical, operational, MDM, and AI-ready datasets.\nDesign models that balance normalization, dimensional modeling, medallion/lakehouse concepts, and application-specific consumption needs.\nCreate denormalized reporting and semantic-model-ready structures that simplify business consumption and reduce ambiguity for AI/LLM use cases.\nProcess and optimize large data volumes in Snowflake using efficient SQL, PL/SQL-style procedural logic, Snowflake Scripting, and performance-aware design.\nCreate reusable patterns for historical tracking, snapshots, audit columns, data versioning, and lifecycle management.\nEnsure data models support downstream BI, AI/ML, semantic models, data apps, MDM Explorer/Entity 360 use cases, and enterprise reporting.\n Required Skills and Experience\n10+ years of experience in data engineering, database engineering, analytics engineering, or data platform development in production environments.\nStrong hands-on experience with Snowflake, including architecture, performance tuning, security design, cost optimization, and cost tracking.\nThorough understanding of Snowflake design patterns for analytical workloads, high-volume data processing, data sharing, and multi-environment deployments.\nHands-on experience with ETL/ELT tools; dbt experience is strongly preferred.\nStrong SQL and PL/SQL-style development experience, including complex transformations, stored procedures, performance tuning, and large-scale data processing.\nPython experience for data automation, API integration, file handling, data validation, metadata processing, or operational tooling.\nExperience designing and implementing enterprise data models, curated data layers, semantic layers, and reusable data products.\nExperience with data integration patterns across enterprise applications, APIs, files, cloud storage, operational systems, MDM platforms, and analytical platforms.\nWorking understanding of Master Data Management concepts such as golden records, crosswalks/XREFs, match/merge, survivorship, hierarchies, entity relationships, stewardship, and data quality.\nExperience partnering with MDM, Product, or business teams to translate mastering requirements into source-to-target mappings, transformation logic, validations, and downstream data consumption patterns.\nAbility to work directly with cross-functional stakeholders to gather requirements, explain design tradeoffs, and drive alignment.\nExperience implementing data quality, lineage, auditability, observability, and operational monitoring within data pipelines.\nComfortable operating as a hands-on senior individual contributor who can also influence strategy and engineering standards.\nPreferred Experience\nExperience with Reltio MDM, including inbound data loads, outbound exports, Reltio APIs, crosswalks, match/merge outputs, survivorship outputs, and operational troubleshooting.\nExperience in life sciences, healthcare, pharma commercialization, HCO/HCP mastering, patient data, channel data, customer master, or commercial data platforms.\nExperience with life sciences reference and commercial datasets such as HIN, DEA, NPI, NCPDP, 340B/PHS, 844, 852, 867, chargebacks, gross-to-net, government pricing, PBR, or UBR.\nExperience with orchestration frameworks such as Airflow, Dagster, dbt Cloud jobs, cloud-native schedulers, or similar tools.\nExperience with cloud platforms and storage patterns, especially Azure or AWS object storage integrated with Snowflake.\nExposure to AI-ready data architecture, feature stores, ML datasets, semantic models, or AI/ML pipeline enablement.\nExperience with Terraform, CI/CD, Git-based development, and infrastructure-as-code practices.\nSnowflake SnowPro, Reltio, dbt, or equivalent cloud/data engineering certifications.\n What does IntegriChain have to offer?\nMission driven: Work with the purpose of helping to improve patients' lives! \nExcellent and affordable medical benefits + non-medical perks including Flexible Paid Time Off and much more!\nRobust Learning & Development opportunities including over 700+ development courses free to all employees\nIntegriChain is committed to equal treatment and opportunity in all aspects of recruitment, selection, and employment without regard to race, color, religion, national origin, ethnicity, age, sex, marital status, physical or mental disability, gender identity, sexual orientation, veteran or military status, or any other category protected under the law. 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