Technical Architect - Data

Overview

We are looking for a seasoned Data Technical Architect to work with our team and our clients to design, architect, and implement enterprise-grade data platforms, services, pipelines, data models, visualizations, and advanced analytics solutions. The Data Technical Architect must be a technologist with excellent communication and customer service skills and a passion for data, innovation, and problem solving. This role spans the full data lifecycle, including data architecture, data engineering, analytics, AI/ML enablement, and cloud platform modernization.

The ideal candidate will provide technical leadership in defining enterprise data strategies, architecting scalable data solutions, and guiding teams through successful implementation.

Responsibilities include:

• Design greenfield data solution stacks in the cloud or on-premises using modern data services, technologies, and industry best practices.
• Architect the migration and modernization of legacy data environments with a focus on scalability, performance, reliability, and security.
• Define enterprise data architecture standards, integration patterns, and technical roadmaps supporting business and mission objectives.
• Design conceptual, logical, and physical data models to support transactional, analytical, and AI/ML workloads.
• Assess and understand data sources, data models, schemas, data quality requirements, and data workflows to support enterprise data initiatives.
• Assess, understand, and design ETL/ELT processes, data pipelines, orchestration workflows, and data integration solutions.
• Lead architecture reviews and provide technical oversight to engineering teams implementing data solutions.
• Establish data governance, metadata management, data quality, data lineage, and security best practices across enterprise data platforms.
• Assess, understand, and design reporting solutions, business intelligence architectures, data visualization strategies, and executive dashboards.
• Evaluate and recommend BI and analytics platforms, ensuring alignment with business and technical requirements.
• Assess, understand, and design machine learning and AI solutions, MLOps pipelines, and supporting infrastructure for data science teams.
• Address technical inquiries concerning customization, integration, enterprise architecture, and the functionality of data products and platforms.
• Design and implement cloud-based data lakehouse architectures leveraging AWS services or equivalent Azure and GCP technologies.
• Architect solutions involving relational databases, data warehouses, data lakes, streaming platforms, and distributed data systems.
• Collaborate with stakeholders, engineers, data scientists, and business teams to translate requirements into scalable technical solutions.
• Support Agile software development life cycles and modern Dev Sec Ops , DBOps, and MLOps practices.
• Provide technical leadership, mentorship, and architectural guidance to project teams.
• Contribute to the growth and advancement of our AI & Data Exploitation Practice.

• Ability to hold a position of public trust with the U.S. Government.
• Bachelor's degree and 10+ years of relevant experience.
• 5+ years of experience designing and architecting enterprise-scale data platforms, data warehouses, data lakes, and lakehouse solutions.
• 5+ years industry experience developing commercial software and solving complex technical challenges.
• 5+ years direct experience delivering enterprise data solutions with technologies such as:
  • Big Data Technologies:
    Hadoop, Spark, Kafka, Databricks, or other distributed data processing platforms.
  • Databases:
    Relational databases including PostgreSQL, MySQL, Microsoft SQL Server, and Oracle. No
    
    SQL databases including MongoDB and similar technologies.
  • Data Integration & Orchestration:
    Airflow, NiFi, or other workflow orchestration and pipeline management platforms.
  • Cloud Platforms: AWS services such as EC2, EMR, RDS, Redshift, Glue, Sage Maker and related analytics services or equivalent Azure and GCP services.
  • Streaming Technologies:
    Kafka, Spark Structured Streaming, Kinesis, Pub/Sub, Event Hubs, or similar real-time streaming technologies.
  • Data Science &…