Principal Data Platform Engineer Jobs in Washington, DC

Role Title: Principal Data Platform Engineer

Location: Arlington, VA (HYBRID)
Duration: Long Term

Job Description

Position Summary:

Provides contracted technical architecture, implementation, and advisory support for Client’s enterprise data platform on Snowflake — the governed, analytics-ready foundation for reporting, analytics, and AI/ML across the organization. Supports the design, build, governance implementation, operational readiness, and cost-management framework for the platform during the engagement. Works closely with Client stakeholders and the third-party delivery team to provide hands-on technical expertise, accelerate implementation, document decisions and standards, and transfer knowledge to Client’s internal data warehouse and reporting team. Supports data consumer enablement, semantic layer standards, and governed data availability for AI workloads in partnership with Client’s AI Organization.

Essential Duties and Responsibilities:

Support the architecture and implementation of the Snowflake data platform — including environment design, Bronze / Silver / Gold medallion layering, enterprise data model recommendations, and standards that promote consistency as the platform grows.

Provide senior technical guidance on platform changes, helping ensure new pipelines and sources align with established patterns and avoid unnecessary one-off solutions.

Assist with the infrastructure-as-code foundation, including AWS provisioning via CloudFormation and Snowflake configuration, RBAC, and schema objects deployed through CI/CD, so the environment is version-controlled and repeatable.

Develop and recommend platform performance and cost-management practices — including query and workload optimization, warehouse sizing, resource monitors, and workload-level cost visibility — to support responsive performance and predictable, attributable spend.

Design, build, and document ELT pipelines that ingest, transform, and curate data from structured and unstructured source systems into the medallion architecture, and provide technical review of contributions from the delivery partner and internal team as requested.

Recommend and help implement engineering standards — code review, testing, deployment, and documentation — for adoption by Client’s internal team and any embedded contractors.

Support operational readiness for the platform and pipelines, including monitoring, alerting, incident-response procedures, resolution guidance for data delivery failures, and documented runbooks that enable Client staff to operate the platform independently.

Implement data quality controls — validation, reconciliation against sources, and quality monitoring — and drive remediation with source-system owners when issues originate upstream.

Support the implementation of the platform’s governance posture by translating Client’s data governance standards into recommended platform controls, including role-based access, recertification processes, data classification with masking and row-level protections, retention and deletion practices, and audit-ready lineage and access evidence.

Provide technical recommendations and implementation support to help the platform meet Client’s security and compliance requirements, including PrivateLink connectivity and relevant data-protection obligations.

Serve as a contracted technical counterpart to the third-party delivery team during the build, participating actively in design and implementation activities while supporting real-time knowledge transfer to Client personnel.

Provide knowledge transfer, documentation, and technical coaching to help Client’s internal data engineering team operate, maintain, and extend the platform after the engagement.

Enable data consumers — supporting BI and analytics teams, publishing governed data products (curated datasets, data marts, and consumption schemas) for self-service and citizen development, and maintaining the documentation that makes the platform usable without gatekeeping.

Set and enforce standards for the semantic layer — ensuring business definitions are modeled once, consistently, and pushed into governed data structures rather than duplicated across individual reports.

Advise IT leadership and business stakeholders on translating enterprise data and analytics priorities into implementation sequencing, future source onboarding considerations, and potential AI/ML workload readiness.

Partner with Client’s AI Center of Excellence to make governed data available to AI workloads — curating AI-ready data assets, establishing patterns for retrieval and search, and evaluating emerging Snowflake capabilities such as Cortex AI, semantic models, and vector-based structures — with the CoE owning AI solution delivery.

Responsible for the proper security and disposal of any confidential information that he or she may possess in the course of performing this position’s job duties, in accordance with Client’s Personnel & Administrative Policy and HIPAA Privacy and Security Policies & Procedures Manuals.

Direct Reports to this Position:

None. This contracted role provides senior technical expertise, implementation support, standards recommendations, documentation, and knowledge transfer without direct supervisory responsibility.

Formal Education Required:

Bachelor’s degree in computer science or related field.

Experience and Certifications Required:

Minimum of 10 years of progressive experience in data engineering, data warehousing, or related technical roles, including at least five (5) years of hands-on experience architecting and building on Snowflake.

Demonstrated experience implementing a medallion (Bronze / Silver / Gold) or comparable layered architecture, operating in an infrastructure-as-code model, and deploying through Git-based CI/CD workflows.

Experience serving as a technical lead or senior point of accountability on a data platform or program, including setting standards and reviewing the work of others.

Experience implementing platform observability, monitoring, alerting, and operational dashboards.

Snowflake certification (e.g., SnowPro Core or Advanced) and/or relevant AWS certifications preferred.

Knowledge, Skills and Abilities Required (as demonstrated by prior work experience):

Deep Snowflake expertise, including warehouse design, RBAC, performance tuning, and cost management.

Full understanding of the Snowflake Data and AI Cloud — Cortex / Snowpark ML, Snowflake Openflow, Snowpipe, Snowpark, SnowSQL, Snowflake Horizon, and Snowflake data warehousing.

Strong proficiency in Python and advanced SQL, with the ability to design pipelines that are testable, observable, and maintainable.

Experience with cloud architectures, including AWS in a data context (e.g., S3, IAM, networking/PrivateLink).

Familiarity with the end-to-end data analytics workflow, from source ingestion through transformation, semantic modeling, and BI consumption (e.g., Power BI).

Strong data modeling and data warehousing fundamentals, with knowledge of databases and modern ELT patterns.

Working command of data governance and security practices — access control design, data classification, PII handling, retention, and lineage.

Problem-solving skills applicable to large-scale (“big data”) environments, with the ability to diagnose and resolve complex data and platform issues.

Ability to communicate technical decisions and trade-offs, both verbally and in writing, to technical and non-technical audiences in a clear and precise manner.

Ability to work effectively alongside an external delivery partner and to facilitate knowledge transfer and technology evaluation.

Ability to provide service excellence by building relationships, being resourceful, responsive and respectful.

Essential Physical Requirements:

The worker is required to have close visual acuity to perform an activity such as: preparing and analyzing data and figures; transcribing; viewing a computer terminal and extensive reading.

Exerting up to 20 pounds of force occasionally, and/or up to 10 pounds of force frequently, and/or a negligible amount of force constantly to move objects. If the use of arm and/or leg controls requires exertion of forces greater than that for sedentary work and the worker sits most of the time, the job is rated for light work.