Data Modeler
这是一个远程位置。设计概念,逻辑和物理数据模型 (ER 图表,维度/星际方案,适用的数据库)对于中流数据域管道规划和提名,体积测量和分配,收集和处理,终端/存储库存,关税和监管报告以及商业合同。拥有铜 →银 →黄金层配合策略与数据工程合作:定义原料/阶段对符合企业实体相比呈现层事实和尺寸的属性。构建和维护维度模型 (事实/尺寸设计,SCD 类型 1/2战略,替代品相比). 定义和维护企业数据词典,实体关系文档以及列层的元数据 (商业界定义,有效值,数据系)对中流主题领域。评估通过 Informatica IDMC/CDIR 和 dbt 获得的源系统,并模型它们如何将其调整成单一一致视图。与业务利益相关者进行数据模型审查 (商业,日程安排,会计,监管),以验证这些模型的完善性 支持核心中流参考实体 (资产,计量器/测量点,合同,对方,关税表)的总数据管理。为数据治理做出贡献:评估新数据来源或业务变化 (新的管道系统,收购,监管变化)对现有模型的影响并提出避免破坏下游消费者的方案演进策略。要求资格5年以上的数据建模经验 (概念性,逻辑性,物理),包括维度建模和正常化的/3NF 设计。中流石油与天然气领域直接经验实践接触至少一个:管道规划/提名,量度测量和分配,气体/液体平衡,终端或储存运营或监管报告 云数据仓库 (Snowflake, Redshift, BigQuery 等)的 SQL 技能强大以及经验建模。
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This is a remote position.
Design conceptual, logical, and physical data models (ER diagrams, dimensional/star schemas, Data Vault where applicable) for midstream data domains — pipeline scheduling and nominations, volumetric measurement and allocations, gathering and processing, terminal/storage inventory, tariffs and regulatory reporting, and commercial contracts.Own the Bronze → Silver → Gold layering strategy in partnership with data engineering: define what belongs in raw/staging vs. conformed business entities vs. presentation-layer facts and dimensions.Build and maintain dimensional models (fact/dimension design, SCD Type 1/2 strategy, surrogate vs. natural keys, conformed dimensions across business units) for reporting and analytics consumption.Partner with data engineers to translate models into dbt sources, staging models, and marts, and review PRs for adherence to modeling and naming standards.Define and maintain the enterprise data dictionary, entity relationship documentation, and column-level metadata (business definitions, valid values, data lineage) for midstream subject areas.Evaluate source systems ingested via Informatica IDMC/CDIR and dbt, and model how they reconcile into a single conformed view.Lead data model reviews with business stakeholders (commercial, scheduling, accounting, regulatory) to validate that models correctly represent contracts, allocations, and volumetric relationships.Support master data management for core midstream reference entities (assets, meters/measurement points, contracts, counterparties, tariff schedules).Contribute to data governance: naming conventions, modeling standards, data quality rules, and stewardship processes.Assess the impact of new data sources or business changes (new pipeline systems, acquisitions, regulatory changes) on existing models and propose schema evolution strategies that avoid breaking downstream consumers.Required Qualifications5+ years of data modeling experience (conceptual, logical, physical), including dimensional modeling and normalized/3NF design.Direct experience in midstream oil & gas — hands-on exposure to at least one of: pipeline scheduling/nominations, volumetric measurement and allocations, gas/liquids balancing, terminal or storage operations, or regulatory reporting Strong SQL skills and experience modeling for a cloud data warehouse (Snowflake, Redshift, BigQuery, or similar).Practical experience with dbt (or a comparable transformation framework) — understands how models translate into source()/ref() dependencies, tests, and documentation.Solid grasp of Kimball-style dimensional modeling (star schema, slowly changing dimensions, conformed dimensions, factless facts) and when to use Data Vault or 3NF instead.Experience with data modeling tools (erwin, ER/Studio, SqlDBM, dbdiagram, or similar) and producing clear ER diagrams for both technical and business audiences.Comfortable working directly with business SMEs to extract and formalize business rules into a data model — this role requires translating ambiguous operational knowledge into structured schemas, not just implementing a spec handed to you.Understanding of data governance fundamentals: metadata management, data lineage, data quality rules, and stewardship.Preferred QualificationsExperience with Informatica IDMC (or similar ETL/ELT and CDC replication tooling) and understanding of how ingestion patterns (batch vs. CDC) affect downstream model design.Experience with Airflow or another orchestration platform, and how DAG design affects data freshness and dependency management for the models you own.Exposure to medallion/lakehouse architecture (Bronze/Silver/Gold) and the tradeoffs of layering conformed business models vs. raw ingestion.Prior experience in a data governance or data steward capacity.
Originally posted on Himalayas