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.
Role Summary
We are looking for a dynamic and visionary Practice Lead to build and scale our Data & AI capabilities with a strong focus on Databricks, Apache Spark, Iceberg, Azure Data Fabric, Amazon EMR, and Redshift. This role is pivotal in driving Lakehouse architecture adoption, cloud-native modernization, and ML engineering across industries such as Oil & Gas, Medtech, Retail CPG, and Fintech.
You will lead a cross-functional team of architects, engineers, and ML specialists to deliver high-impact solutions that combine technical excellence, cost efficiency, and governance maturity.
Key Responsibilities
Strategic Leadership & Practice GrowthDefine and execute the strategic roadmap for the Data & AI practice, aligning with evolving trends in Lakehouse, cloud-native platforms, and AI engineering.
Build reusable solution accelerators (e.g., Iceberg ingestion templates, Spark ETL frameworks, MLflow pipelines).
Establish and manage strategic partnerships with Databricks, AWS, Azure, and other ecosystem players.
Represent the practice in industry forums, client workshops, and thought leadership initiatives.
Technical Architecture & Delivery OversightArchitect scalable Lakehouse solutions using Delta Lake or Iceberg, optimized for performance and cost.
Lead the design of unified batch + streaming pipelines using Spark Structured Streaming and Scala.
Oversee multi-cloud data platform modernization using Azure Data Fabric, EMR, and Redshift.
Guide implementation of data governance frameworks using Unity Catalog, Azure Purview, and Iceberg metadata.
Team Leadership & Capability BuildingMentor and grow a high-performing team of data engineers, ML engineers, and platform specialists.
Define career paths, training plans, and certification goals aligned with Databricks, AWS, and Azure.
Foster a culture of innovation, experimentation, and continuous improvement.
Client Engagement & Business ImpactEngage with clients to understand business challenges and translate them into scalable data solutions.
Lead solutioning, proposal development, and delivery for strategic engagements.
Ensure delivery excellence through performance optimization (Photon, EMR Autoscaling) and cost engineering.
Required Skills & Experience
10+ years of experience in data engineering, architecture, and platform modernization.
3+ years of hands-on experience with Databricks, Apache Spark, Iceberg, and Scala.
Deep expertise in cloud-native data platforms (Azure Data Fabric, Amazon EMR, Redshift).
Proven track record in building ML pipelines, implementing MLOps (MLflow), and integrating Redshift ML.
Strong understanding of data governance, observability, and schema evolution.
Excellent leadership, communication, and stakeholder management skills.
Preferred Qualifications
Certifications in Databricks, Azure Data Engineering, or AWS Big Data.
Experience in vertical-specific data solutions (e.g., predictive maintenance in Oil & Gas, real-time analytics in Retail).
Familiarity with data mesh architectures, time travel, and audit compliance frameworks.
Why Join Us?
Be at the forefront of Lakehouse innovation and AI-driven transformation.
Work with cutting-edge technologies and shape the future of data platforms.
* Lead a high-impact practice with global reach and industry relevance.
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