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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Enterprise Data Architect - **Company:** Qode LLC - **Location:** Cleveland, OH, United States - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Amazon Web Services, Business Analytics Applications, Data Analysis, JIRA, Big Data, Cloud Computing, Cloud Database, Information Systems, Data Architecture, Data Cleansing, Information Engineering, Data Governance, Extract Transform Load (ETL), Data Transformation, Distributed Data Store, Apache Hadoop, Hadoop Distributed File System, MapReduce, Apache HBase, Apache Hive, Python (Programming Language), Machine Learning, Scrum Methodology, Systems Development Life Cycle, Scala (Programming Language), Sqoop, Data Streaming, Enterprise Data Management, Data Processing, Cloud Platform System, Real Time Systems, Feature Engineering, Data Ingestion, Large Language Models, Apache Spark, Apache Flume, Kubernetes, Information Technology, Data Analytics, Enterprise Integration, Apache Kafka, Data Management, Machine Learning Operations, Stream Processing, Data Pipelines - **Published:** May 19, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=ad3b67afc52d547a ## About the Role * 12+ years of experience in data engineering, data analytics, or enterprise data consulting. * Strong hands-on experience with big data and distributed data platforms. * Proficiency in Python, with experience in streaming and real-time data processing. * Solid understanding of data modeling, ETL/ELT design, and data quality practices. * Experience supporting cloud-based data platforms, preferably AWS. * Exposure to machine learning, AI, and MLOps concepts preferred. * Experience working in Agile/Scrum environments. * Strong communication and consulting skills with experience working in client-facing roles. * Bachelor's or Master's degree in Computer Science, Information Systems, Engineering, or related field. ## Description Role: Enterprise Data Architect Location: Dallas, Pittsburgh, Cleveland Experience: 12+ years Duration: Full time We are seeking an Enterprise Data Consultant to support the design, delivery, and optimization of large-scale data engineering, analytics, and AI-enabled solutions across the enterprise.This role partners closely with business, technology, and architecture teams to translate complex data requirements into scalable, secure, and compliant solutions. Key Responsibilities:Enterprise Data Analysis & Solution Delivery * Partner with business and technology stakeholders to analyze enterprise data requirements and translate them into scalable data engineering and analytics solutions. * Design, build, and support end-to-end data pipelines, including data ingestion, preprocessing, normalization, transformation, quality checks, and loading across complex data ecosystems. * Lead and contribute to ETL/ELT development using technologies such as Spark, Hadoop, Hive, Kafka, Python, and Scala, ensuring performance, reliability, and data accuracy. Data Platforms & Architecture * Work with distributed data platforms including HDFS, HBase, Sqoop, Flume, and MapReduce, supporting both batch and real-time processing use cases. * Apply strong data modeling and data design principles to support analytics, reporting, regulatory, and operational needs. * Collaborate with enterprise architects on logical and physical data models aligned with PNC standards. Data Quality, Governance & Compliance * Support and implement data quality frameworks, including profiling, validation rules, reconciliation, and monitoring to ensure trusted and compliant data. * Collaborate with cross-functional teams to ensure solutions align with enterprise architecture, security, governance, and regulatory requirements. Cloud, Analytics & AI Enablement * Contribute to cloud-based data solutions, particularly on AWS, supporting data processing, analytics, and ML workloads. * Collaborate with data scientists and ML engineers to enable machine learning and AI use cases, including feature engineering, data preparation, and pipeline integration. * Support development and deployment of ML and AI systems, including exposure to LLM-based solutions, feature stores, and ML lifecycle management tools. MLOps & Agile Delivery * Participate in or support MLOps practices, including model deployment, monitoring, retraining pipelines, and integration with platforms such as SageMaker, MLflow, Kubeflow, or similar tools. * Work in Agile delivery environments, actively participating in sprint planning, stand-ups, reviews, and retrospectives using tools such as Jira. Stakeholder Engagement & Consulting * Serve as a client-facing consultant, coordinating across the SDLC and communicating technical concepts clearly to both technical and non-technical stakeholders. * Contribute to solutioning, estimations, POCs, and client proposals, helping shape data, analytics, and AI modernization initiatives. People & Capability Development * Mentor junior team members, support onboarding, and promote best practices in data engineering, analytics, and platform design. * Foster collaboration across teams to support continuous improvement and delivery excellence. ## Related Videos - [Alibaba Big Data and Machine Learning Technology](https://www.wearedevelopers.com/videos/37-alibaba-big-data-and-machine-learning-technology) - [Modern Data Architectures need Software Engineering](https://www.wearedevelopers.com/videos/1030-modern-data-architectures-need-software-engineering) - [Improving quality with Agentic AI with Rovo Dev and Xray](https://www.wearedevelopers.com/videos/2005-improving-quality-with-agentic-ai-with-rovo-dev-and-xray) - [Collaboration Quantified: Lessons from Open Source Developer Networks](https://www.wearedevelopers.com/videos/1422-collaboration-quantified-lessons-from-open-source-developer-networks) - [PySpark - Combining Machine Learning & Big Data](https://www.wearedevelopers.com/videos/44-pyspark-combining-machine-learning-big-data) - [From Messy Queries to Scalable Systems - How Data Engineering actually works](https://www.wearedevelopers.com/videos/100203-from-messy-queries-to-scalable-systems-how-data-engineering-actually-works) ## Related Articles - [Making Data Warehouses Fast: A Developer’s Story](https://www.wearedevelopers.com/magazine/107-making-data-warehouses-fast-a-developer-s-story) - [Top Big Data Technologies That You Need to Know](https://www.wearedevelopers.com/magazine/108-top-big-data-technologies-that-you-need-to-know) - [Highest Paying Tech Companies for Developers](https://www.wearedevelopers.com/magazine/220-highest-paying-tech-companies-for-developers) - [The Most Popular IT Jobs on the Market](https://www.wearedevelopers.com/magazine/376-the-most-popular-it-jobs-on-the-market) - [Coffee with Developers - Maria Apazoglou - Making AI understandable for all in production](https://www.wearedevelopers.com/magazine/475-coffee-with-developers-maria-apazoglou-making-ai-understandable-for-all-in-production) - [Data Engineer Salary UK](https://www.wearedevelopers.com/magazine/253-data-engineer-salary-uk)