Location: Dhahran, Saudi Arabia
Muller's Solutions is seeking an experienced
Data Engineer
with
5–6 years of experience
in designing, developing, and maintaining scalable data solutions. The ideal candidate should possess strong expertise in
Python, Pandas, NumPy, Airflow, BigQuery, SQL, and API development
. The candidate will play a key role in building robust data pipelines, processing large datasets, and supporting business-critical applications related to inventory optimization, master data management, and backend services.
Key Responsibilities
Design, develop, and maintain scalable
ETL/ELT pipelines
for data ingestion and processing.
Build and optimize data workflows using
Apache Airflow
.
Develop and maintain data processing solutions using
Python
,
Pandas
, and
NumPy
.
Design and optimize complex
SQL queries
and data models in
BigQuery
.
Develop and integrate APIs to support data exchange between systems.
Implement data ingestion frameworks from various internal and external sources.
Perform data cleansing, transformation, and validation to ensure data accuracy and consistency.
Develop and maintain processes for
master data management
.
Support inventory optimization initiatives by implementing data processing and business logic.
Build and maintain backend services that enable data-driven applications.
Monitor and troubleshoot data pipelines to ensure reliability and performance.
Collaborate with cross-functional teams, including Product, Analytics, and Engineering teams.
Ensure adherence to data governance, security, and best practices.
Requirements
Technical Requirements
Must-Have Skills
5–6 years of experience in Data Engineering or related roles.
Strong hands-on experience with:
Python
Pandas
NumPy
Apache Airflow
Google BigQuery
SQL
API development and integration
Experience in designing and implementing scalable
ETL/ELT pipelines
.
Strong understanding of data modeling, data transformation, and database concepts.
Experience working with large-scale datasets and optimizing query performance.
Proficiency with version control systems such as
Git
.
Good-to-Have Skills
Experience with
PySpark
and distributed data processing.
Basic understanding of
Machine Learning concepts and workflows
.
Familiarity with
data quality frameworks
and data validation practices.
Experience with
Docker
and containerization technologies.
Knowledge of
CI/CD pipelines
and DevOps practices.
Experience working in cloud-based data environments.
Preferred Qualifications
Bachelor's degree in Computer Science, Information Technology, Data Science, or a related field.
Strong analytical and problem-solving skills.
Excellent communication and collaboration abilities.
Experience working on enterprise-scale data platforms and data-driven applications.
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