Data Engineering Consultant (Category - Architect)

At Codvo, we are committed to building scalable, future-ready data platforms that power business impact. We believe in a culture of innovation, collaboration, and growth, where engineers can experiment, learn, and thrive. Join us to be part of a team that solves complex data challenges with creativity and cutting-edge technology.

Enterprise Data Architecture & Design:

•            Define and maintain the enterprise data architecture blueprint, including data lakes, warehouses, streaming platforms, and analytics layers.

•            Design logical, physical, and conceptual data models to support operational, analytical, and AI use cases.

•            Ensure architecture supports scalability, performance, and high availability.

•            Architect & Build Data Pipelines: Design, construct, install, test, and maintain highly scalable data management systems and ETL/ELT pipelines.

•            Integrate Diverse Data Sources: Develop processes to ingest and integrate high-volume, high-velocity data from SCADA systems, historians (like OSIsoft PI, Aspen InfoPlus.21), DCS, PLC, and IoT sensors.

•            Cloud Data Platform Development: Implement and manage data solutions on the Microsoft Azure cloud platform, Leveraging services like Azure IoT Hub, Azure Event Hubs, and Azure Stream Analytics for real-time ingestion and processing of operational technology (OT) data.

•            Data Modelling & Warehousing: Design and implement data models optimized for time-series data from industrial assets, supporting operational dashboards and real-time analytics.

•            Enable Advanced AI: Build the data infrastructure to support AI/ML models for predictive maintenance, operational anomaly detection, and process optimization using real-time OT data.

•            10+ years of experience in data architecture, data engineering, or enterprise analytics roles.

•            Strong experience designing enterprise-scale data platforms in cloud and hybrid environments.

•            Expertise in data modelling (conceptual, logical, physical) and data integration patterns.

•            Deep understanding of data lakes, data warehouses, Lakehouse architectures, and real-time streaming platforms.

•            Hands-on experience with cloud data services (Azure preferred).

•            Strong knowledge of SQL, data transformation frameworks, and metadata management.

•            Familiarity with DevOps and DataOps practices.

•            Strong communication skills with the ability to engage both technical and business stakeholders.

•            Certifications such as Azure Data Engineer Expert, Azure Solutions Architect, or equivalent.

•            Hands-on experience with the OSDU™ Data Platform.

•            Experience in Oil & Gas, energy, or industrial data environments.

•            Understanding of cybersecurity considerations for OT environments and data segregation.

•            Experience integrating data from ERP systems like SAP.

•            Experience working within AI-first or analytics-driven transformation programs.

•            Advanced SQL skills, including query optimization and performance tuning.

•            Experience of constructing and maintaining enterprise knowledge graphs.

Note- Please apply via our official careers portal only, as applications sent directly to executives may not be considered. 

•            Expert-level proficiency in SQL and Python for data manipulation and pipeline development.

•            Big Data Technologies: Hands-on experience with distributed computing frameworks like Apache Spark (PySpark). Experience with streaming technologies like Kafka is a plus.

•            Cloud Platforms: Deep experience with Microsoft Azure (Azure Data Lake Storage, Azure Data Factory, Azure Databricks, Azure Synapse).

•            Data Warehousing/Lakehouse: Proven experience with modern data platforms Databricks Delta Lake.

•            AI & ML Knowledge: Understanding of machine learning lifecycles and the data requirements for training and deploying AI/ML models.

•            Version Control: Proficiency with Git and CI/CD best practices.