GCP Data & AI Engineer - Secure, Scalable Pipelines

BuddoBot Inc.

Herndon (VA)

Hybrid

USD 120,000 - 190,000

Full time

6 days ago

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Job summary

Dark Wolf seeks a Data & AI Engineer specializing in Google Cloud Platform to architect, deploy, and scale data, ML, and generative AI workflows in secure cloud environments. You will lead architectural development, scaling, and governance of enterprise-grade data platforms, transforming unstructured data into production-ready assets for agentic search and multi-modal AI systems.

You will design secure data meshes, automate data quality, optimize distributed compute, and ensure high resilience

Qualifications

Bachelor's degree in computer science or a related field (or equivalent practical experience).
4+ years of data engineering experience.
4+ years of experience in architecting data structures for model grounding and engineering pipelines that transform raw data into tool-accessible assets for agentic workflows.
4+ years of experience with Python, Scala, SQL, and Go for high-performance data platform development.
4+ years of experience with distributed systems internals, LMS trees, column-family stores, and data warehouse and lakehouse storage formats.
4+ years of experience with Kubernetes cluster management, custom operators for data platforms, and service mesh integration.
4+ years of experience with enterprise data strategy, conceptual-to-physical data modeling, and streaming data schema contracts.
4+ years of experience on GCP supporting AI/ML research, experimentation, and production delivery.
Experience and expertise in Apache Spark, Flink, Kafka, Ray, and workflow orchestration engines.
Experience architecting multi-cloud and hybrid cloud data platform solutions (IaC via Terraform).
Understanding of DevSecOps pipelines, automated compliance testing, and secret rotation management.
Strong understanding of GCP services and technologies.
Must be a U.S. Citizen holding an active Secret Security Clearance.

Responsibilities

Lead the architectural design and implementation of cloud-native distributed, high-throughput batch and streaming data platforms.
Optimize distributed execution engines to handle petabyte-scale datasets under strict SLAs.
Implement and collaborate on Infrastructure as Code (IaC) to provision data platform assets across multi-cloud infrastructure.
Establish zero-trust security controls, data lineage tools, and automated compliance scanning for regulated data environments.
Design and manage ETL/ELT pipelines using cloud-native, Google services such as Cloud Storage, Dataflow, Cloud Run, Agent Platform, and BigQuery.
Understand big data processing frameworks for large-scale data sets such as Apache Spark.
Architect and process large-scale datasets and cloud data warehouses using tools such as Google BigQuery and Knowledge Catalog.
Programming experience to build automation scripts, handle data structures and connect to APIs.
Configure and manage GCP services such as Compute Engine, App Engine, Kubernetes Engine, Cloud Storage, BigQuery, Dataflow, and Cloud SQL.
Enforce cloud security best practices and support DoD/DISA Authority to Operate (ATO) processes.
Act as Technical Pod Lead, driving Agile spring deliverables, establishing technical standards, and mentoring data engineering staff.
Stay up-to-date on the latest GCP services and technologies.
Work collaboratively within a fast paced Agile team environment.