Data Engineer

Data Engineer (AI & Cloud Platforms)

Location: Hybrid

Travel: 20% or less

About the job

OakTruss Group is seeking a Data Engineer (AI & Cloud Platforms) to support the design, development, and maintenance of scalable data systems across internal and client-facing initiatives. This role may be assigned across a variety of projects based on business needs, priorities, and areas of focus.

This position is ideal for a data engineering professional with a strong engineering mindset and hands-on experience building modern data pipelines, cloud-based platforms, and AI-ready data architectures. The role contributes at both the engineering and solutioning levels, with a strong emphasis on performance, reliability, governance, and secure deployment.

Externally, this individual may engage with clients as a technical contributor, helping translate business and data requirements into practical engineering solutions. Internally, this role will partner with leadership, PMO, and cross-functional teams to support data-driven capabilities aligned with company strategy.

Key responsibilities

• Design, build, and maintain scalable data pipelines and ETL/ELT workflows for large-scale structured and unstructured datasets
• Develop and manage data warehouses, data lakes, and lakehouse environments
• Build and optimize serverless query solutions using AWS Athena to support cost-effective analysis of data stored in S3
• Help ensure data quality, lineage, and governance across data assets
• Optimize query performance and storage for cost and speed
• Collaborate with data scientists and ML engineers to build and deploy production-ready ML pipelines
• Build feature engineering pipelines and support maintenance of feature stores to enable AI/ML model development
• Integrate LLM-based tools, vector databases, or AI APIs into data workflows where applicable
• Support model monitoring, retraining pipelines, and data drift detection
• Support the architecture and management of cloud-based data infrastructure on AWS, GCP, or Azure
• Leverage AWS services including Athena, Glue, S3, and Redshift to build end-to-end data solutions
• Implement infrastructure-as-code practices using tools such as Terraform, dbt, or similar technologies
• Build and maintain orchestration workflows using Airflow, Prefect, or similar tools
• Partner cross-functionally with analytics, product, and engineering teams to define data requirements
• Document data models, pipeline architecture, and best practices for internal knowledge sharing
• Contribute to engineering standards, code reviews, and continuous improvement efforts
• Support and mentor junior engineers as appropriate
• Support technical execution of data-related engagements in alignment with Statements of Work
• Help ensure timely delivery of data solutions, documentation, and supporting materials
• Collaborate with PMO and leadership to manage priorities, timelines, and deliverables
• Identify and escalate technical risks across projects
• Contribute to internal tools, accelerators, reusable assets, and knowledge sharing
• Stay current with advancements in data engineering, cloud platforms, AI/ML operations, and related technologies

Qualifications

• Bachelor’s degree in computer science, engineering, data science, information systems, or a related technical field
• 5+ years of professional experience in data engineering or related technical roles, including:
• Experience designing and maintaining scalable data pipelines and modern data platforms
• 1–2 years supporting AI/ML-adjacent workflows, ML pipelines, or model enablement
• Consulting or client-facing technical experience preferred

Preferred technical experience

• Proficiency in Python and SQL
• Experience with Spark or distributed computing is a plus
• Hands-on experience with AWS, including Athena, Glue, S3, and Redshift
• Experience building and managing data warehouses, data lakes, or lakehouse environments
• Familiarity with cloud platforms such as AWS, Azure, or GCP and scalable deployment concepts
• Experience with orchestration tools such as Airflow, Prefect, or similar platforms
• Familiarity with infrastructure-as-code and transformation tooling such as Terraform, dbt, or similar technologies
• Familiarity with MLOps tools and concepts such as MLflow, Kubeflow, SageMaker, or similar platforms
• Understanding of data quality, governance, lineage, and storage optimization principles
• Exposure to LLM-based tools, vector databases, or AI APIs within data workflows is a plus

Professional attributes

• Ability to contribute at both technical and advisory levels with credibility in both spaces
• Strong collaboration skills and the ability to work effectively across technical and business teams
• Strong communication skills, with the ability to translate technical concepts into clear, actionable insights
• Strong problem-solving and systems-thinking capabilities
• High level of ownership, accountability, and attention to detail
• Ability to work independently while contributing to initiatives under guidance and deadlines
• Commitment to delivering high-quality, scalable, and efficient solutions

Why join us

• Opportunity to work across modern data engineering, cloud platform, analytics, and AI-adjacent initiatives
• Exposure to meaningful internal and client-facing work
• Collaborative environment with cross-functional partnership
• Strong opportunity to contribute to scalable, high-quality technical solutions