Data Engineer

Work Location: Detroit, MI

About the role

We are seeking an experienced Snowflake Data Engineer to design, build, optimize, and support scalable enterprise data solutions on the Snowflake platform. This role sits within a Banking & Financial Services client environment and requires deep hands-on expertise in Snowflake, dbt, SQL, and Python, with dbt being a core and mandatory requirement.

Responsibilities

• Design, develop, and maintain scalable data pipelines and transformation workflows using Snowflake, dbt, SQL, and Python.
• Build and maintain enterprise-grade dbt models including staging, intermediate, dimensional, and consumption-layer models; develop reusable macros, tests, snapshots, seeds, and packages.
• Implement incremental processing strategies in dbt for large-volume datasets ensuring accuracy, restartability, and performance.
• Design and optimize Snowflake data models supporting analytical, operational, financial, and regulatory reporting requirements.
• Develop complex SQL transformations using joins, window functions, CTEs, aggregations, MERGE operations, and advanced analytical functions.
• Optimize Snowflake queries by analyzing Query Profile, micro-partition pruning, clustering, warehouse sizing, and query execution behavior.
• Develop ingestion and ELT pipelines to load structured and semi-structured data from multiple enterprise source systems.
• Implement data-quality and reconciliation controls using dbt tests, custom tests, SQL validation, and automated monitoring.
• Implement historical data-processing patterns including Slowly Changing Dimensions, snapshots, effective dating, and audit-history requirements.
• Implement Snowflake features including Streams, Tasks, Dynamic Tables, Snowpipe, Time Travel, Zero-Copy Cloning, Secure Views, and Secure Data Sharing.
• Implement Snowflake security controls including RBAC, masking policies, row-access policies, and least-privilege access.
• Implement CI/CD practices for Snowflake and dbt deployments across DEV, TEST, UAT, and PROD environments using Git-based workflows.
• Develop and maintain technical documentation including source-to-target mappings, dbt documentation, transformation logic, data-flow diagrams, runbooks, and operational procedures.
• Troubleshoot and resolve data-pipeline failures, dbt execution failures, Snowflake performance problems, and production defects.
• Collaborate with Data Architects, business analysts, reporting teams, data scientists, governance teams, and application teams to translate requirements into reliable data solutions.
• Mentor junior data engineers and promote engineering best practices across the team.

Requirements

• Bachelor's degree in Computer Science, Information Technology, Engineering, or a related discipline.
• 8–12 years of overall experience in data engineering, data warehousing, or ETL/ELT development.
• 4+ years of hands-on Snowflake experience preferred.
• Strong hands-on dbt experience is mandatory, ideally including enterprise-scale dbt implementations.
• Demonstrated experience building production-grade Snowflake data pipelines and transformation solutions.
• Experience supporting data solutions through the complete software-development lifecycle in large enterprise environments.

Skills

• Snowflake: Strong hands-on enterprise experience; deep understanding of Virtual Warehouses, micro-partitions, partition pruning, clustering, Query Profile, result caching, warehouse sizing, auto-suspend/resume, and multi-cluster warehouses; experience with performance tuning, query optimization, security (RBAC, masking, row-level access), and cost optimization.
• dbt (Mandatory): Strong hands-on experience designing and developing large-scale dbt projects; building staging, intermediate, fact/dimension, data mart, and incremental models; implementing all materializations (table, view, incremental, ephemeral); developing reusable Jinja macros; creating custom and generic tests; implementing source freshness checks; managing snapshots, schema evolution, and dbt packages; integrating dbt into CI/CD pipelines; using dbt Core or dbt Cloud in production environments.
• SQL: Advanced SQL skills including complex joins, CTEs, window functions, analytical functions, MERGE, aggregations, subqueries, and data reconciliation patterns; ability to optimize complex SQL workloads against very large datasets.
• Python: Strong working knowledge for data-engineering use cases including ingestion, validation, file processing, API integration, automation, and pipeline orchestration; familiarity with Snowflake Python connectors and/or Snowpark Python desirable.
• Data Engineering: Strong understanding of ETL/ELT architecture, data warehousing, data lakes, dimensional modeling, star schemas, SCDs, incremental processing, CDC, data-quality frameworks, metadata management, and data lineage.
• Banking & Financial Services: Experience in a highly governed enterprise environment; understanding of PII, sensitive financial data, auditability, regulatory reporting, data retention, and access controls.
• Data Governance & Security: Experience implementing Dynamic Data Masking, Row Access Policies, Secure Views, audit logging, data lineage, and data retention controls.
• CI/CD & DevOps: Experience with Git-based source control, branching strategies, pull requests, and CI/CD tooling such as GitHub Actions, Azure DevOps, GitLab CI, or Jenkins.

Preferred Qualifications

• Snowflake SnowPro certification.
• dbt certification or substantial enterprise dbt implementation experience.
• Banking or Financial Services domain experience.
• Experience with Snowpark, Snowpipe, and/or streaming ingestion.
• Experience with orchestration technologies such as Airflow, Azure Data Factory, or AWS Step Functions.
• Exposure to machine-learning and advanced-analytics use cases.
• Experience mentoring junior or mid-level data engineers.