Data Engineer (BigQuery)

Northramp LLC•Washington, DC

1d•$135,000 - $165,000•Hybrid

About The Position

Northramp is seeking a Data Engineer to support a mission-critical effort to consolidate, modernize, and operate a client's enterprise cloud services across IaaS, PaaS, and SaaS environments. The role designs and operates cloud-native data pipelines, warehousing, and integration layers that make data accessible, reliable, and analytics-ready. BigQuery is the primary analytical data warehouse, supporting enterprise reporting, AI/ML, and program management data needs. The position is part of Northramp’s integrated delivery model, in which engineers and advisors work together on high-stakes modernization programs. It is hybrid and based in the Washington, DC metro area, with on-site presence at designated client locations as program needs require. Remote work is supported subject to mission and security requirements. Candidates must be located in the DC, Maryland, or Virginia region. The ideal candidate has production experience building reliable data pipelines, designing schemas and ingestion for real-world source systems, and treating data quality as an engineering discipline. The candidate should also be comfortable working within federal access controls and compliance requirements. DHS suitability and a Public Trust background investigation are required; active Public Trust or higher clearance is preferred. Selected applicants will be subject to a security investigation and may need to meet eligibility requirements for access to controlled or classified information.

Requirements

3 to 6 years of progressive, hands-on data engineering experience focused on cloud data platforms and pipeline development.
Bachelor’s degree in Computer Science, Data Engineering, Information Systems, Mathematics, or a related field; relevant experience may substitute.
Strong SQL skills and hands-on experience with BigQuery as an analytical data warehouse, including familiarity with optimization techniques such as partitioning, clustering, and materialized views.
Proficiency in Python for data engineering tasks.
Working knowledge of data services from at least one major cloud provider.
Understanding of data modeling principles and schema design for analytical workloads.
Familiarity with data governance concepts, including cataloging, lineage, access controls, and retention, and their application in regulated environments.
Knowledge of FedRAMP, FISMA, and NIST 800-53 data security requirements.
U.S. citizenship and ability to obtain and maintain a DHS suitability / Public Trust clearance.
Eligible to work in the United States.

Nice To Haves

Experience with dbt for data transformation and modeling in cloud warehouse environments.
Hands-on experience with pipeline orchestration tools such as Apache Airflow or Cloud Composer.
Google Cloud Professional Data Engineer certification.
AWS Certified Data Analytics Specialty or Azure Data Engineer Associate certification.
Security+ or equivalent certification.
Experience with streaming data platforms such as Pub/Sub, Kafka, or Kinesis.
DHS or other federal data engineering experience.
Active Public Trust or higher clearance.

Responsibilities

Design, build, and maintain scalable ELT/ETL pipelines that ingest structured and semi-structured data from cloud services, APIs, databases, and files into BigQuery and other cloud data warehouses.
Develop and manage data models, schemas, and transformation logic using dbt, SQL, and Python, and enforce testing and documentation standards across the data layer.
Implement and operate pipeline orchestration using Apache Airflow (Cloud Composer), Prefect, or equivalent; monitor pipeline health, SLA adherence, and failure alerting.
Integrate data platforms with upstream systems, including AWS RDS, Azure SQL, GCS/S3, operational databases, and third-party SaaS APIs.
Implement data access controls, column-level security, and encryption in BigQuery and related cloud storage services in alignment with FedRAMP High and FISMA requirements.
Build and maintain data cataloging and lineage metadata practices to support governance and auditability.
Collaborate with data scientists and analysts to ensure data availability, schema stability, and analytical workload performance.
Develop and maintain infrastructure as code for data platform components using Terraform and participate in CI/CD integration for data assets.
Support data quality frameworks, including profiling, anomaly detection, and SLA monitoring, across production pipelines.
Contribute to ATO documentation and data security controls, including data classification, retention policies, and audit logging.