Data Engineer; BigQuery

Opportunity Overview

Northrampis seeking a Data Engineer to join the team supporting a mission-critical effort to consolidate, modernize, andoperateour client'senterprise cloud services across IaaS, PaaS, and SaaS environments.

You will design andoperatecloud-native data pipelines, warehousing, and integration layers that make the client's data accessible, reliable, and analytics-ready. The role centers on modern cloud data platforms — with

BigQuery as the primary analytical data warehouse — supportingtheclient'senterprise reporting, AI/ML, and program management data needs.

This role is part of Northramp’s integrated delivery model, where engineers and advisors work as one team to bring sound judgment, disciplined execution, and deep federal experience to high-stakes modernization programs.

Location & Work Arrangement

Hybrid, based in the Washington, DC metro area. On-site presence at designated client locations is expected on a cadence aligned to program needs. Remote work is supported around mission and security requirements. This role is not open to candidates outside the DC, Maryland, and Virginia region.

The Ideal Candidate

You build data pipelines that run reliably in production — not just demos. You’ve designed schemas and ingestion pipelines against messy, real-world source systems, you understand data quality as an engineering discipline, and you can work comfortably within the access controls and compliance requirements of a federal environment.

Key Responsibilities

• Design, build, and maintain scalable ELT/ETL data pipelines ingesting structured and semi-structured data from cloud services, APIs, databases, and file sources into Big Query and other cloud data warehouses.

• Develop and manage data models, schemas, and transformation logic using dbt (data build tool), SQL, and Python; 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 source systems including cloud-native services (AWS RDS, Azure SQL, GCS/S3), operational databases, and third-party SaaS APIs.

• Implement data access controls, column-level security, and encryption within Big Query and related cloud storage services aligned to FedRAMP High and FISMA requirements.

• Build and maintain data cataloging and lineage metadata practices to support data governance and auditability requirements.

• Collaborate with Data Scientists and analysts to ensure data availability, schema stability, and performance of analytical workloads.

• Develop and maintain infrastructure-as-code for data platform components using Terraform; participate in CI/CD pipeline integration for data assets.

• Support data quality frameworks — profiling, anomaly detection, and SLA monitoring — across production data pipelines.

• Contribute to ATO documentation and data security controls including data classification, retention policies, and audit logging.

Required Qualifications

• 3 to 6 years of progressive, hands-on experience in data engineering with a focus 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 Big Query as an analytical data warehouse; familiarity with Big Query optimization (partitioning, clustering, materialized views).

• Proficiency in Python for data engineering tasks (ingestion, transformation, pipeline scripting).

• Experience with dbt for data transformation and modeling in cloud warehouse environments.

• Hands-on experience with pipeline orchestration tools:
  Apache Airflow, Cloud Composer, or equivalent.

• Working knowledge of cloud storage and data services across at least one major cloud provider (GCS, S3, Azure Blob Storage, Cloud Spanner, Redshift, or equivalent).

• Understanding of data modeling principles (dimensional modeling, data vault, or similar) and schema design for analytical workloads.

• Familiarity with data governance concepts — cataloging, lineage, access…