Senior Data Engineer (PySpark ML Pipelines)

Location: Dubai WFO

Notice: Immediate/Serving notice - 30 days only.

Skill: PySpark + ML Pipeline + Banking Domain

Experience: 7+ years

Role: Senior Data Engineer

:

1. The Data Engineer role involves acquiring requirements, conducting EDA, ingesting required datasets, and transforming data using Big-data technologies & Feature Engineering techniques for machine learning models.

2. Key tasks include building high-performance, secure, and scalable data pipelines, collaborating with Analytics Delivery Leads, Data scientists and other teams in an Agile environment, and communicating stakeholders effectively.

3. The position requires a degree in a relevant field, at least 8-10 years of experience in data engineering and ML pipelines, and proficiency in technologies like Python, Spark (including expertise in optimization techniques), Hadoop, SQL, and Git.

4. Should be good with Python as well.

Roles and Responsibilities:

• Data Pipeline Development: Design, develop, and maintain highly scalable and optimized ETL pipelines using PySpark on the Cloudera Data Platform, ensuring data integrity and accuracy.

• Data Ingestion: Implement and manage data ingestion processes from a variety of sources (e.g., relational databases, APIs, file systems) to the data lake or data warehouse on CDP.

• Data Transformation and Processing: Use PySpark to process, cleanse, and transform large datasets into meaningful formats that support analytical needs and business requirements.

• Performance Optimization: Conduct performance tuning of PySpark code and Cloudera components, optimizing resource utilization and reducing runtime of ETL processes.

• Data Quality and Validation: Implement data quality checks, monitoring, and validation routines to ensure data accuracy and reliability throughout the pipeline.

• Automation and Orchestration: Automate data workflows using tools like Apache Oozie, Airflow, or similar orchestration tools within the Cloudera ecosystem.

• Monitoring and Maintenance: Monitor pipeline performance, troubleshoot issues, and perform routine maintenance on the Cloudera Data Platform and associated data processes.

• Collaboration: Work closely with other data engineers, analysts, product managers, and other stakeholders to understand data requirements and support various data-driven initiatives.

• Documentation: Maintain thorough documentation of data engineering processes, code, and pipeline configurations.

Education and Experience

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

• 7+ years of experience as a Data Engineer, with a strong focus on PySpark and the Cloudera Data Platform. Technical Skills

• PySpark: Advanced proficiency in PySpark, including working with RDDs, DataFrames, and optimization techniques.

• Cloudera Data Platform: Strong experience with Cloudera Data Platform (CDP) components, including Cloudera Manager, Hive, Impala, HDFS, and HBase.

• Data Warehousing: Knowledge of data warehousing concepts, ETL best practices, and experience with SQL-based tools (e.g., Hive, Impala).

• Big Data Technologies: Familiarity with Hadoop, Kafka, and other distributed computing tools.

• Orchestration and Scheduling: Experience with Apache Oozie, Airflow, or similar orchestration frameworks.

• Scripting and Automation: Strong scripting skills in Linux.

• Good at Data Modelling as well. Soft Skills

• Strong analytical and problem-solving skills.

• Excellent verbal and written communication abilities.

• Ability to work independently and collaboratively in a team environment. Attention to detail and commitment to data quality.

Note: Looking for immediate to 30 days official Notice period candidates only.

Interested candidates please share your CV to [HIDDEN TEXT] with below details:

Total Exp:

Relevant Exp in PySpark:

Relevant Exp in ML Pipeline:

Relevant Exp in Banking Domain:

Current CTC:

Expected CTC:

Current location:

Preferred location:

Notice period:

Highest Education (fulltime):