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 Overview
We are looking for a highly skilled Data Engineer a deep understanding of asset-heavy industries such as oil & gas, manufacturing, or utilities and expertise in Databricks to design, develop, and optimize end-to-end data pipelines, data platforms, and analytics solutions. This role combines strong data engineering, cloud platform expertise, and software engineering skills to deliver scalable, production-grade solutions.
Key Responsibilities
Design and develop ETL/ELT pipelines on Databricks (PySpark, Delta Lake, SQL).
Architect data models (batch and streaming) for analytics, ML, and reporting.
Optimize performance of large-scale distributed data processing jobs.
Implement CI/CD pipelines for Databricks workflows using GitHub Actions, Azure DevOps, or similar.
Build and maintain APIs, dashboards, or applications that consume processed data (full-stack aspect).
Collaborate with data scientists, analysts, and business stakeholders to deliver solutions.
Ensure data quality, lineage, governance, and security compliance.
Required Skills & Qualifications
Core Databricks Skills:
Strong in PySpark, Delta Lake, Databricks SQL.
Experience with Databricks Workflows, Unity Catalog, and Delta Live Tables.
Programming & Full Stack:
Python (mandatory), SQL (expert).
Exposure to Java/Scala (for Spark jobs).
Knowledge of APIs, microservices (FastAPI/Flask), or basic front-end (React/Angular) is a plus.
Cloud Platforms:
Proficiency with at least one: Azure Databricks, AWS Databricks, or GCP Databricks.
Knowledge of cloud storage (ADLS, S3, GCS), IAM, networking.
DevOps & CI/CD:
Git, CI/CD tools (GitHub Actions, Azure DevOps, Jenkins).
Containerization (Docker, Kubernetes is a plus).
Data Engineering Foundations:
Data modeling (OLTP/OLAP).
Batch & streaming data processing (Kafka, Event Hub, Kinesis).
Data governance & compliance (Unity Catalog, Lakehouse security).
Nice-to-Have
Experience with machine learning pipelines (MLflow, Feature Store).
Knowledge of data visualization tools (Power BI, Tableau, Looker).
Exposure to Graph databases (Neo4j) or RAG/LLM pipelines.
Qualifications
Bachelor's or Master's in Computer Science, Data Engineering, or related field.
4-7 years of experience in data engineering, with at least 2 years on Databricks.
2+ years of experience in asset-heavy industries (e.g., oil & gas, utilities, manufacturing).
Soft Skills
Strong problem-solving and analytical skills.
Ability to work in fusion teams (business + engineering + AI/ML).
Clear communication and documentation abilities.
Domain - Oil & Gas
Location - Doha, Qatar
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