Data Engineer / Modeler

Overview

Remote
Depends on Experience
Contract - W2
Contract - 12 Month(s)
No Travel Required
Unable to Provide Sponsorship

Skills

Data Engineer / Modeler
Snowflake
warehouses
roles
Tasks/Streams
shares
performance tuning
ELT/ETL pipelines
Python
SQL
RBAC
masking
PII handling

Job Details

Role: Data Engineer / Modeler 

Location: Remote

Duration: Long Term 

        
Own end-to-end data flow from source systems into a semantic data model in Snowflake. Analyze current lineage and workflows, automate robust ELT/ETL pipelines and implement monitoring to ensure accuracy, timeliness and reliability. 
Key Responsibilities 
        · Map current-state lineage from sources through staging, transformation, and the semantic model in Snowflake; document dependencies, SLAs, and data contracts. 
        
        · Design and automate ELT/ETL pipelines(e.g., SQL/Python, dbt/Snowflake Tasks/Streams) to standardize ingest, transformations and loads. 
        
        · Implement data quality & observability(tests, thresholds, freshness, volume, schema change detection) with alerting and runbooks. 
        
        · Establish monitoring dashboards and on-call procedures; investigate and resolve incidents; drive root-cause analysis and prevention. 
        
        · Continuously optimize cost/performance (warehouse sizing, pruning, clustering, caching) and security (RBAC, masking, PII handling). 
        
Qualifications:

        · 4–7+ years in data engineering with Snowflake (warehouses, roles, Tasks/Streams, shares, performance tuning). 
        
        · Expert SQL and strong Python; experience with dbt or equivalent transformation frameworks. 
        
        · Solid understanding of semantic modeling(star/snowflake, data products, canonical layers) and data governance best practices. 
        
        · Proven ownership of production pipelines: SLAs, incident response, postmortems and stakeholder communication. 
        
Success Metrics (KPIs) 
        · Pipeline SLA adherence & freshness (% on-time loads). 
        
        · Data quality pass rate (tests/failures MTTR/MTBF). 
        
        · Cost/performance improvements (query latency, $/TB). 
        
        · Documentation & lineage coverage (% assets with contracts & lineage). 
        
        · Reduction in incidents/regressions and faster time-to-recovery.

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