Senior Data Engineer (Snowflake | dbt | Airflow)

Location: Remote (Worldwide – Must have at least 6 hours overlap with US Eastern Time)

Compensation: Competitive (Based on experience)

Company Overview

Our client is building a modern data platform that powers business intelligence, product development, and operational decision-making through reliable, scalable, and production-grade data systems.

The engineering culture emphasizes software engineering best practices applied to data. The team values automated testing, observability, reproducible deployment workflows, documentation, and maintainable architecture. Engineers are expected to own quality from development through production and build data platforms that stakeholders can trust.

Working closely with the Lead Data Engineer, you'll help evolve the company's Snowflake ecosystem, implement scalable dbt transformation layers, and build robust ELT pipelines that support teams across Engineering, Product, and Business.

Challenge

We are looking for a Senior Data Engineer with deep expertise in Snowflake, dbt, Airflow, SQL, and Python to help design, build, and scale a modern cloud data platform.

This is a highly technical, hands-on engineering role focused on delivering production-quality datasets and pipelines that are reliable, observable, well-tested, and cost-efficient.

You'll partner closely with the Lead Data Engineer and cross-functional stakeholders to translate business requirements into scalable data models while establishing engineering best practices around testing, CI/CD, documentation, governance, and infrastructure.

Key Responsibilities

• Design, build, and maintain scalable data models and datasets in Snowflake.
• Develop and maintain dbt projects, including models, snapshots, macros, tests, and documentation.
• Design efficient schemas based on business requirements, query patterns, and operational constraints.
• Build and maintain production ELT pipelines using Airflow.
• Integrate managed ingestion tools such as Fivetran and implement incremental loading, Snowpipe, and Change Data Capture (CDC) processes.
• Develop SQL and Python code, including Snowflake Python UDFs when appropriate.
• Implement automated testing using dbt, unit tests, and integration tests.
• Build reliable CI/CD workflows for dbt projects and infrastructure deployments.
• Optimize Snowflake performance through warehouse sizing, clustering strategies, caching, and query optimization.
• Monitor and improve Snowflake cost efficiency.
• Implement observability, monitoring, alerting, lineage tracking, and data quality validation.
• Create and maintain technical documentation, migration guides, runbooks, and architectural decision records.
• Participate in code reviews and contribute to engineering standards and best practices.
• Mentor engineers on Snowflake, dbt, Airflow, testing, and deployment practices.
• Support data governance, access control, masking, and compliance initiatives.

Must Have

• Strong production experience with Snowflake.
• Advanced SQL skills with experience optimizing performance and cost.
• Hands-on experience developing dbt projects (models, macros, snapshots, tests, and documentation).
• Experience building production-grade ELT pipelines.
• Strong experience with Apache Airflow.
• Advanced Python programming skills.
• Experience using Infrastructure as Code to provision and manage Snowflake infrastructure.
• Experience managing relational databases such as PostgreSQL.
• Experience with incremental loading strategies, Snowpipe, and Change Data Capture (CDC).
• Familiarity with Snowflake Streams and Tasks.
• Strong understanding of automated testing, CI/CD, and deployment best practices.
• Experience implementing observability, monitoring, lineage, and data quality frameworks.
• Excellent communication skills and experience collaborating with cross-functional teams.

Nice to Have

• Experience using Terraform with the Snowflake Provider.
• Experience managing multi-environment infrastructure (Development, Staging, Production).
• Experience with dbt Cloud or CI/CD pipelines for dbt deployments.
• Knowledge of data governance, metadata management, masking, and column-level security.
• Experience optimizing Snowflake warehouse utilization and cloud costs.
• Experience mentoring other engineers.
• Experience supporting SOC 2 or similar compliance initiatives.

Soft Skills

• Strong ownership mentality and accountability.
• Passion for engineering quality and maintainable systems.
• Excellent analytical and problem-solving skills.
• Ability to balance engineering excellence with pragmatic decision-making.
• Strong collaboration and stakeholder communication skills.
• Comfortable working independently in a remote environment.
• Attention to detail and commitment to delivering reliable production systems.
• Continuous learning mindset and openness to new technologies, including responsible use of AI-assisted development tools.

Benefits

• Fully remote position.
• Flexible remote-first culture.
• Opportunity to work on a modern cloud-native data platform.
• High ownership over architecture and engineering decisions.
• Collaborative engineering environment with strong technical standards.
• Opportunity to influence data platform strategy and best practices.
• Long-term growth opportunities as the engineering organization scales.

Hiring Process

• Application Review
• Recruiter Interview
• Technical Interview (Snowflake, dbt, SQL, Airflow, and Data Modeling)
• Take-Home Technical Assessment
• Final Interview with the Lead Data Engineer and Stakeholders
• Offer