Data Engineer at CNTXT

Dubai, Dubai, United Arab Emirates -

Full Time

Start Date

Immediate

Expiry Date

17 Dec, 26

Salary

0.0

Posted On

18 Sep, 26

Experience

3 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Industry

Information Technology & Services

Description

About the RoleWe are looking for a skilled Data Engineer to build, optimize, and maintain scalable data infrastructure that powers analytics, AI, and machine learning products. You will be responsible for designing robust data pipelines, integrating data from multiple sources, ensuring data quality, and enabling data accessibility across the organization.This role is ideal for someone who enjoys solving complex data challenges, building modern data platforms, and working closely with software engineers, data scientists, and product teams to transform raw data into business value.Key ResponsibilitiesDesign, develop, and maintain scalable ETL/ELT pipelines for structured and unstructured data.
Build and manage modern data warehouses and data lakes.
Develop reliable batch and real-time data processing pipelines.
Integrate data from APIs, databases, third-party platforms, and cloud services.
Ensure high standards of data quality, integrity, security, and governance.
Optimize data models and database performance for analytics and reporting.
Monitor, troubleshoot, and improve data pipeline reliability and performance.
Collaborate with Data Scientists, ML Engineers, Product Managers, and Software Engineers to deliver data solutions.
Implement CI/CD practices and infrastructure automation for data workflows.
Document data architecture, pipeline designs, and engineering best practices.
Stay current with emerging technologies in cloud data engineering and big data ecosystems.

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Responsibilities

RequirementsBachelor's degree in Computer Science, Software Engineering, Information Systems, or a related field.
4+ years of experience in Data Engineering or Backend/Data Platform development.
Strong proficiency in Python and SQL.
Experience building ETL/ELT pipelines using modern orchestration tools such as Airflow, Prefect, or Dagster.
Strong knowledge of relational and NoSQL databases including PostgreSQL, MySQL, MongoDB, or Cassandra.
Experience with cloud platforms such as AWS, Azure, or Google Cloud Platform.
Hands-on experience with cloud data warehouses such as Snowflake, BigQuery, Amazon Redshift, or Azure Synapse.
Experience with distributed data processing frameworks such as Apache Spark.
Familiarity with message streaming technologies such as Kafka or RabbitMQ.
Experience with Docker, Kubernetes, and CI/CD pipelines.
Strong understanding of data modeling, partitioning, indexing, and performance optimization.
Experience working with Git and collaborative software development workflows.
Preferred QualificationsExperience building data platforms supporting AI or Machine Learning workloads.
Knowledge of Delta Lake, Apache Iceberg, or Apache Hudi.
Experience with dbt for analytics engineering.
Familiarity with Terraform or Infrastructure as Code.
Understanding of data governance, metadata management, and data cataloging.
Experience working in high-growth technology or AI companies.
Technical StackLanguages: Python, SQL
Databases: PostgreSQL, MySQL, MongoDB
Data Processing: Apache Spark, Pandas
Orchestration: Apache Airflow, Prefect, Dagster
Streaming: Kafka, RabbitMQ
Cloud: AWS, Azure, GCP
Data Warehouse: Snowflake, BigQuery, Redshift, Synapse
Containerization: Docker, Kubernetes
Version Control: Git
Infrastructure: Terraform (preferred)
What We're Looking ForStrong analytical and problem-solving skills.
Excellent understanding of scalable data architecture.
Ability to work independently in a fast-paced environment.
Strong communication and collaboration skills.
Passion for building reliable, high-performance data systems.
Continuous learning mindset and enthusiasm for modern data technologies.
Nice to HaveExperience supporting Generative AI or LLM applications.
Experience with vector databases such as Pinecone, Weaviate, or Milvus.
Familiarity with data observability platforms such as Monte Carlo or Great Expectations.
Experience with event-driven architectures and real-time analytics.
Exposure to MLOps platforms and feature stores.