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Elfonze TechnologiesListed on Himalayas
Data Modeler
Remote · United States
Source: Himalayas. This job was published on Himalayas; you apply there.
RemoteFull-timeMid levelDeveloper
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Estimated pay
Estimate$137K–$240K/yr
Estimate — the employer hasn't published pay.
Based on 914 similar mid-level Software Engineering jobs in United States that show pay. The range is the middle half of their posted pay (25th to 75th percentile), not an offer — ask the employer for the real range.
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At a glance
- Experience asked
- 5+ years
- Education
- Master's
Skills mentioned
dbtaccountingairflowbigquerysnowflakesql
About the role
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 Qualifications
5+ 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 Qualifications
Experience 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