Data Engineer

Description

The company is a federal-focused technology firm that serves US government clients in defense, national security, public safety, civilian, and military health.

The team works within a larger global consulting organization and offers hands-on learning, certifications, and industry training.

Responsibilities

• Build, maintain, and tune batch and streaming pipelines that feed analytics and AI workloads.
• Ingest structured, semi-structured, and unstructured data from APIs, databases, SaaS systems, streams, and files.
• Clean, enrich, and standardize data using Dataflow (Apache Beam), Dataproc (Spark), BigQuery SQL, and Python.
• Deliver curated datasets into BigQuery for analytics, reporting, and model training.
• Build and maintain ML-ready feature pipelines for Data Scientists and ML Engineers.
• Add data quality checks, schema validation, and automated tests inside pipelines.
• Monitor pipeline health with Cloud Monitoring and Cloud Logging, following observability practices.
• Apply governance and security controls such as IAM roles, encryption, data masking, and auditing.
• Enforce schema evolution rules, metadata management, and lineage tracking with Dataplex or Data Catalog.
• Document datasets, transformations, pipeline logic, and operational procedures.
• Write efficient, maintainable Python, SQL, Beam, and Spark code.
• Manage ingestion flows using Pub/Sub, GCS, APIs, Datastream, and database connectors.
• Tune BigQuery tables, partitions, clustering, materialized views, and query performance.
• Build and maintain DAGs in Cloud Composer (Airflow).
• Investigate pipeline failures, latency problems, and data quality gaps.
• Take part in code reviews, architecture discussions, and agile sprint ceremonies.
• Work with Data Architects, Data Scientists, ML Engineers, and business stakeholders.
• Build and maintain Infrastructure as Code in Terraform and CI/CD deployment pipelines.

Requirements

• You must be a U.S. citizen able to obtain a Public Trust clearance.
• A bachelor's degree in computer science, software engineering, information systems, data engineering, or a related technical field.
• 3 to 6+ years of hands-on data engineering or similar technical experience.
• At least three years leading technical teams to deliver results.
• Experience creating and applying technical standards for cloud and on-premises environments.
• A track record of running production data pipelines on cloud platforms, preferably GCP.
• Hands-on work with BigQuery, GCS, Dataflow (Apache Beam), Dataproc (Spark), and Pub/Sub.
• Experience preparing ML-ready datasets for model training.
• Strong skills in SQL, Python, distributed data processing, and data modeling.
• Experience with governance, security, and compliance frameworks, including IAM, encryption, data masking, and auditing.
• Familiarity with VAEC operational tools: Google Cloud security, monitoring and logging, networking, and storage services.

Conditions

Preferred: a master's degree in a technical field.

Preferred: prior work in federal government environments.

Preferred: knowledge of regulated environments such as FedRAMP, HIPAA, PCI, NIST 800-53, and CIS benchmarks.

Preferred: a security certification such as CISSP or CCSP.

Preferred: experience with Vertex AI workflows or comparable ML platforms.

Preferred: familiarity with Dataplex, data governance frameworks, and metadata management.

Preferred: experience with Apache Kafka or other streaming technologies.

Preferred: experience with Datastream for change data capture (CDC).

Preferred: knowledge of regulated industries such as public sector, healthcare, or finance.

Preferred: strong communication skills and the ability to explain complex data concepts clearly.

Preferred: experience with BI tools such as Looker or Looker Studio.

Preferred: experience with third-party tools such as Armis, BigFix, CrowdStrike, Tenable Nessus, Turbot, ServiceNow, Dynatrace, Splunk, and others.

Preferred: hands-on work with DevOps tools and methods, including Ansible, GitHub, Jira, Terraform, CI/CD, and cloud migration tools.

Preferred: knowledge of ML enablement, feature stores, and ML pipeline patterns.

Preferred: experience with data quality and testing frameworks such as Great Expectations or dbt tests.

Applications are accepted on an ongoing basis with no fixed deadline.

Pay range for California, Colorado, Connecticut, Hawaii, Illinois, Maine, Maryland, Massachusetts, Minnesota, New Jersey, New York, Ohio, Vermont, Virginia, Washington, and the District of Columbia: $103,200 - $196,400 USD base pay, varying by office location, role, skill set, and experience.

The company is committed to equal opportunity and an inclusive culture.

Benefits

• A wide range of benefits is offered; details are available from the company.