Daman

Data Architect

⭐ - Featured Role | Apply direct with Data Freelance Hub
This role is a Senior Data Architect for a long-term contract, remote from Erie, PA, with a pay rate of "unknown." Key skills include Snowflake, dbt, SQL, data quality, and data engineering. Experience with data governance and analytics is required.
🌎 - Country
United States
💱 - Currency
$ USD
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💰 - Day rate
Unknown
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🗓️ - Date
January 11, 2026
🕒 - Duration
Unknown
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🏝️ - Location
Remote
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📄 - Contract
Unknown
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🔒 - Security
Unknown
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📍 - Location detailed
United States
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🧠 - Skills detailed
#Documentation #Data Science #Database Schema #Informatica #ML (Machine Learning) #Metadata #Data Engineering #"ETL (Extract #Transform #Load)" #Snowflake #AI (Artificial Intelligence) #SQL (Structured Query Language) #Data Architecture #Data Quality #dbt (data build tool) #Data Catalog #Data Access
Role description
Job Title: Senior Data Architect Job Location: Remote (Erie, PA) Job type: Long-term Contract Role Overview The Full-Stack Data Engineer works within the Data Accessibility Program, supporting the enterprise data platform built on Snowflake and dbt. Snowflake & dbt Responsibilities dbt • ELT transformations and model dependencies • Data tests and documentation • Lineage visibility and change impact analysis Snowflake • Database, schema, and table design • Warehouses, virtual warehouses, and performance tuning • Resource management and cost awareness • OLAP and reporting Data Quality & Governance • Implement data quality controls, balancing, and reconciliation rules using DataGaps. • Monitor data quality trends and remediate issues at the source or transformation level. • Integrate and align with Informatica IDMC for business glossary, metadata cataloging, and governance workflows. Data Services & Analytics Enablement • Design and build data services / APIs that expose curated Gold data as reusable data products. • Use optimized SQL and Snowflake patterns for predictable performance. • Support analytics, reporting, and downstream consumption use cases. Collaboration • Partner with data scientists, product owners, domain SMEs, and architects to translate business needs into data products. • Collaborate with Advanced Analytics teams to enable AI/ML, feature stores, and downstream analytics use cases.