Lead Data Engineer

Candidates must be local to the DC/MD/VA area. This is a hybrid opportunity in Arlington, VA. Key Responsibilities

Support the design and implementation of the data platform, including environment setup and layered architecture standards, to promote growth and consistency.
Provide senior technical guidance on platform changes, ensuring new data pipelines and sources align with established best practices.
Assist with infrastructure-as-code, including cloud provisioning, configuration, RBAC, schema setup, and deployment through CI/CD pipelines.
Develop performance and cost-management strategies such as workload optimization, warehouse sizing, resource monitoring, and cost visibility.
Design, develop, and review ELT pipelines that ingest, transform, and curate data from structured and unstructured sources.
Establish engineering standards for code review, testing, deployment, and documentation for both internal teams and contractors.
Ensure operational readiness through monitoring, incident response, troubleshooting, and creating runbooks for independent platform operation.
Implement data quality controls and drive remediation with source systems to address upstream issues.
Translate data governance standards into platform controls, including role-based access, data classification, masking, retention policies, and audit-lineage.
Support security and compliance requirements, including connectivity and data protection protocols.
Collaborate with third-party delivery teams during platform build, supporting design, implementation, and knowledge transfer.
Provide ongoing documentation, training, and coaching to internal data engineering teams for platform maintenance and extension.
Enable self-service data consumption for BI and analytics teams by publishing governed datasets, data marts, and consumption schemas.
Define and enforce standards for the semantic layer, ensuring consistent business definitions and data structures.
Advise leadership on enterprise data priorities, onboarding future data sources, and preparing AI workloads.
Partner with AI teams to curate AI-ready data assets and evaluate emerging platform capabilities, ensuring secure data handling and disposal.

Bachelor's degree in computer science or a related field.
Minimum of 10 years of progressive experience in data engineering, data warehousing, or similar technical roles.
At least 5 years of hands-on experience architecting and building on Snowflake.
Proven experience implementing layered architecture models, infrastructure-as-code, and deploying via Git-based CI/CD workflows.
Demonstrated experience as a technical lead or senior authority on data platforms, including setting standards and reviewing work.
Experience with platform observability, monitoring, alerting, and operational dashboards.
Certifications such as Snowflake (e.g., SnowPro Core or Advanced) and relevant AWS certifications preferred.
Deep expertise in Snowflake warehouse design, RBAC, performance tuning, and cost management.
Proficiency in Python and advanced SQL, capable of designing observability-driven pipelines.
Experience with cloud architectures, especially AWS, utilizing tools like S3, IAM, and PrivateLink.
Strong understanding of the end-to-end data analytics workflow, data modeling, and modern ELT patterns.
Knowledge of data governance, security practices, data classification, lineage, and PII handling.
Excellent problem-solving skills for large-scale data environments.
Strong verbal and written communication skills for collaborating with technical and non-technical teams.
Ability to work effectively with external partners and facilitate knowledge transfer.
Service-oriented mindset with resourcefulness, responsiveness, and professionalism.

Snowflake certification (e.g., SnowPro Core or Advanced) and relevant AWS certifications.
Experience with modern data workflows involving semantic modeling and BI integration tools like Power BI.
Familiarity with emerging platform features such as Cortex AI, Snowpark ML, and vector-based structures.
Knowledge of data security and compliance standards related to data privacy and protection.
Ability to diagnose and resolve complex data and platform issues in large-scale environments.

For applications and inquiries, contact:View email address on us.fitly.work