Senior Data Engineer building and maintaining data pipelines for cloud and AI solutions at Qodea. Collaborating with ML engineers and focusing on reliability and performance in a cloud - native environment.
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.
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