Data Engineer/Architect

Data Engineer/Architect

Location: Titusville, NJ (Onsite)

Architecture & Design

Design and maintain end-to-end data architecture leveraging Snowflake as the cloud data warehouse

Define data modeling standards (dimensional, hybrid, Data Vault, semantic layers)

Architect scalable, cost-efficient Snowflake environments (databases, schemas, warehouses)

Establish strategies for data ingestion, transformation, and consumption

Review and approve technical designs and data pipelines

Data Modelling Design Pipelines using CoCo

Generate AI Insights using Cortex AI

DBT Development & Governance

Lead implementation using Datacoves

Design and enforce dbt project structure, naming conventions, and folder standards

Develop robust dbt models, macros, snapshots, and seeds

Implement and maintain dbt tests (freshness, generic, custom)

Enable auto-generated documentation and data lineage using dbt docs

Establish CI/CD practices for dbt deployments

Data Engineering & Optimization

Optimize Snowflake performance using: Clustering & micro-partitioning

Query optimization

Warehouse sizing and scaling

Monitor and control Snowflake cost usage

Collaborate with ingestion tools

Implement ELT best practices

Security, Compliance & Reliability

Implement Snowflake security controls (RBAC, masking policies, row-level security)

Ensure adherence to data governance, compliance, and privacy standards

Design data quality frameworks and SLAs

Enable auditability and observability across the data platform

Leadership & Collaboration

Act as a technical advisor to data engineers and analytics teams

Mentor junior engineers and analysts

Translate business requirements into scalable technical solutions

Partner with stakeholders to define KPIs and semantic layers

Contribute to platform roadmaps and architecture decisions

Required Qualifications

8+ years of experience in data engineering or analytics engineering

8+ years of hands-on Snowflake architecture experience

6+ years of dbt (Core or Cloud) experience in production environments

Strong SQL expertise (advanced transformations and performance tuning)

Deep understanding of ELT architectures

Experience with cloud platforms (AWS, Azure, or GCP)

Proficiency with Git-based version control and CI/CD pipelines

Experience with data modeling methodologies