Data Engineer (6 months contract)

Contract duration: 6 months (with a potential extension)
Engagement Type: Full-time
Start date: July 2026
Location: Abu Dhabi (on-site)

Role Overview

We are seeking an experienced Data Engineer to design, build, and operate scalable, secure, and reliable data pipelines and platforms that power analytics and AI solutions for companies in the UAE. The role is hands-on and client-facing, owning data solutions end-to-end — from architecture and development to production and hypercare – on cloud and on-premise.

Key Responsibilities

• Design, build, and maintain scalable batch and streaming data pipelines (ETL/ELT) that move data reliably from source systems to analytics and AI workloads.

• Build and optimize data models, databases, data warehouses, and lakehouse architectures (e.g., Databricks, Microsoft SQL Server) for performance and cost.

• Develop and tune distributed processing jobs using Spark/PySpark, including cluster management and optimization.

• Ensure data quality and data governance across the full data lifecycle (collection, access, storage, transformation, retention).

• Productionize pipelines with CI/CD, orchestration (e.g., Airflow, Azure Data Factory), monitoring, and alerting.

• Build the data foundation for analytics and AI use cases, collaborating closely with Data Scientists and AI Engineers (e.g., feature engineering, RAG data processing).

• Translate client business requirements into data architecture decisions and communicate trade-offs to technical and non-technical stakeholders.

• Maintain technical documentation, data lineage, and delivery artifacts across Design, Build, UAT, Production & Hypercare.

Candidate Requirements

• Demonstrated experience delivering AI solutions in an enterprise or client-facing/consulting environment.

• Prior experience at management consulting firms and/or Big Tech is an advantage.

• Strong programming in Python and SQL with solid software engineering fundamentals.

• Hands-on experience with Spark/PySpark and big-data processing at scale.

• Proven experience building production ETL/ELT pipelines.

• Practical knowledge of a major cloud data stack — Azure, AWS, or GCP.

• Experience with a modern data platform (Databricks, Snowflake, or equivalent).

• Familiarity with orchestration, CI/CD, and DevOps (e.g., Airflow, Git, Docker).

• Understanding of data warehousing concepts, data modeling, data governance, and security best practices.

• Experience with dashboard or frontend engineering is a plus.

• Strong collaboration and communication skills, with the ability to work directly with clients.

Qualifications

• Bachelor's or Master’s degree in Computer Science, Engineering, Information Technology, or a related field.

• 3–6 years of experience in data engineering, building and operating data pipelines at scale.

• Preferred certifications: cloud data certifications such as Azure Data Engineer, AWS Data Engineer, or Databricks Data Engineer.