Senior Data Engineer - Abu Dhabi, UAE

Job Title:

Senior Data Engineer

Key Requirements:
4-8 years of experience
from tier 1 or 2 big tech companies

Job Location:
Abu Dhabi, UAE

Benefits:
Work with cutting-edge technology through modern infrastructure and automation projects
Thrive in a growth-focused environment that prioritizes learning, innovation, and career development
Competitive salary and a comprehensive benefits package

Job Summary:

As a

Senior Data Engineer , you will be responsible for designing, developing, and maintaining advanced, scalable data systems that power critical business decisions. You will lead the development of robust data pipelines, ensure data quality and governance, and collaborate across cross-functional teams to deliver high-performance data platforms in production environments. This role requires a deep understanding of modern data engineering practices, real-time processing, and cloud-native solutions.

Key Responsibilities:
Data Pipeline Development & Management:

Design, implement, and maintain

scalable and reliable data pipelines

to ingest, transform, and load structured, unstructured, and real-time data feeds from diverse sources.
Manage data pipelines for

analytics and operational use , ensuring data integrity, timeliness, and accuracy across systems.
Implement

data quality tools and validation frameworks

within transformation pipelines.
Data Processing & Optimization: Build efficient, high-performance systems by leveraging techniques like

data denormalization ,

partitioning ,

caching , and

parallel processing .
Develop stream-processing applications using

Apache Kafka

and optimize performance for

large-scale datasets .
Enable

data enrichment

and

correlation

across primary, secondary, and tertiary sources.

Cloud, Infrastructure, and Platform Engineering:

Develop and deploy data workflows on

AWS or GCP , using services such as S3, Redshift, Pub/Sub, or BigQuery.
Containerize data processing tasks using

Docker , orchestrate with

Kubernetes , and ensure production-grade deployment.
Collaborate with platform teams to ensure scalability, resilience, and observability of data pipelines.

Database Engineering : Write and optimize complex

SQL queries

on

relational

(Redshift, PostgreSQL) and

NoSQL

(MongoDB) databases.
Work with

ELK stack

(Elasticsearch, Logstash, Kibana) for search, logging, and real-time analytics.
Support

Lakehouse architectures

and hybrid data storage models for unified access and processing.

Data Governance & Stewardship:

Implement robust

data governance ,

access control , and

stewardship

policies aligned with compliance and security best practices.
Establish metadata management, data lineage, and auditability across pipelines and environments.

Machine Learning & Advanced Analytics Enablement:

Collaborate with data scientists to prepare and serve features for ML models.
Maintain awareness of ML pipeline integration and ensure data readiness for experimentation and deployment.

Documentation & Continuous Improvement:

Maintain thorough documentation including

technical specifications ,

data flow diagrams , and

operational procedures .
Continuously evaluate and improve the data engineering stack by adopting new technologies and automation strategies.

Required Skills & Qualifications:
8+ years

of experience in data engineering within a production environment.
Advanced knowledge of

Python

and

Linux shell scripting

for data manipulation and automation.
Strong expertise in

SQL/NoSQL databases

such as PostgreSQL and MongoDB.
Experience building

stream processing systems using Apache Kafka .
Proficiency with

Docker

and

Kubernetes

in deploying containerized data workflows.
Good understanding of

cloud services

(AWS or Azure).
Hands-on experience with

ELK stack

(Elasticsearch, Logstash, Kibana) for scalable search and logging.
Familiarity with

AI models

supporting data management.
Experience working with

Lakehouse systems ,

data denormalization , and

data labeling

practices.

Preferred Qualifications:
Working knowledge of

data quality tools ,

lineage tracking , and

data observability

solutions.
Experience in

data correlation , enrichment from external sources, and managing

data integrity at scale .
Understanding of

data governance frameworks

and enterprise

compliance protocols .
Exposure to CI/CD pipelines for data deployments and infrastructure-as-code.

Education & Experience:
Bachelor’s or Master’s degree in

Computer Science ,

Engineering ,

Data Science , or a related field.
Demonstrated success in designing, scaling, and operating data systems in

cloud-native

and

distributed environments .
Proven ability to work collaboratively with cross-functional teams including product managers, data scientists, and DevOps.

If you are interested in this exciting opportunity, please don't hesitate to apply.