Technical support role ensuring reliability of blue light network services through incident resolution and collaboration with teams. Resolving technical issues in the Data Networks team for Managed Services Contract.
About the role
- Enterprise Data Architect supporting design and optimization of large-scale data engineering and analytics solutions. Collaborating with teams to translate data requirements into scalable, secure, compliant solutions.
Responsibilities
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
Requirements
- 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.
Job title
Job type
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Degree requirement
Tech skills
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