Data Engineering, Analytics & Modeling

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503-IT Full Time Professional Streetsboro, OH, US

The Step2 Company is building a modern data platform on Snowflake, fed by Oracle E-Business Suite and our planning, retail and operations systems. Structured (SQL) and unstructured data land in a medallion architecture (bronze, silver, gold), with Fivetran for ingestion and dbt for transformation. This role owns how that data is structured, modeled and exposed, making Snowflake the trusted source for reporting, planning and analytics across the business.

You will design the tables, models and semantic layer that finance, supply chain, sales and operations rely on every day.

• Pipelines: Build and maintain ELT pipelines using Fivetran and dbt, ingesting SQL sources (Oracle EBS and others) and unstructured data into Snowflake. Organize data through bronze (raw), silver (cleaned and conformed) and gold (business-ready) layers. Includes incremental loads, change capture, dbt tests and scheduling.
• Fivetran management: Own Fivetran syncs for internal and external data sources: connector setup, schedules, schema changes and failure alerts. Keep transfer costs down by choosing the right sync method per source (sync frequency, incremental vs. full loads, table and column selection, Fivetran vs. native Snowflake loading) and tracking usage against budget.
• Source system knowledge: Learn the underlying table structures of our ERP and operational systems and map them to clean, documented models.
• Data quality: Put in place tests, reconciliation checks and monitoring so issues are caught before the business sees them.
• Performance and cost: Tune queries, warehouses, clustering and storage to keep Snowflake fast and within budget.
• Data modeling: Design and maintain dimensional models (star schemas, facts, conformed dimensions) in Snowflake for sales, orders, inventory, production and finance.
• Semantic layer: Work with business teams to capture their terms, definitions, rules and processes, and encode them into semantic models: metrics, relationships, hierarchies, synonyms and business logic. Resolve ambiguous terms (e.g. which "sales" or which date) so that reports, analysts and AI tools get the same deterministic answer to the same question.
• Validated queries: Build a library of automated, validated queries against the semantic models, with expected results checked on a schedule.
• Governed data access for tools and agents: Expose semantic models and validated queries through MCP servers to AI assistants and agents. Monitor what they query, how results perform, and where answers go wrong.
• Power BI migration: Migrate existing Power BI data models into Snowflake semantic models. Repoint Power BI reporting from SQL Server to Snowflake, validating that numbers match before cutover.
• Front-end evaluation: Research and evaluate modern reporting and analytics front ends, both to complement Power BI and as potential replacements for it. Include natural-language query, embedded analytics and agent-driven tools. Run pilots on the Snowflake semantic layer, compare cost and fit against Power BI, and recommend a path, including a migration plan if a replacement is chosen.
• Security and governance: Manage roles, access policies and data classification in Snowflake, working with IT Security.
• Partnering: Work with business users and analysts to turn reporting needs into well-defined models and metrics. Document everything.
• Collaboration: Work with the Manager, Data Analytics on migrating legacy Power BI and SQL Server reporting, and with the Oracle ERP Solutions Architect on the mapping, meaning and governance of ERP data in Snowflake.

• 4+ years in data engineering, data warehousing or analytics engineering.
• Hands-on Snowflake experience: SQL, warehouses, roles and access control, tasks/streams, and cost management.
• Advanced SQL and hands-on dbt (models, tests, documentation, incremental builds). Working knowledge of Python for data processing and automation.
• Experience ingesting data from relational source systems (Oracle, SQL Server) with Fivetran or similar tools, and handling semi-structured and unstructured data (JSON, documents, files). Experience building layered medallion architectures. Understanding of source table structures and keys.
• Strong data modeling skills: dimensional modeling (Kimball),…