Data Engineer
6 days ago
Data Engineer
Location : Dubai
We are looking for Experience upto: 5-8 Years
Summary: Experienced Informatica PC and IDMC Developer with 5+ years in data engineering. Skilled in designing end-to-end data pipelines using PC/IDMC for effective migration and transformation across cloud platforms. The ideal candidate will have in-depth knowledge of Informatica PowerCenter and IDMC. A strong foundation in Data Warehousing concepts and proficiency in Snowflake and SQL is essential. Strong team player with agile experience, delivering timely, high-impact data solutions.
Technical Skills
- Tools: Informatica Cloud Data Integration, Informatica PowerCenter
- Data Warehousing: Snowflake, DataLake
- Programming: SQL, Python, Shell Scripting
- Data Management: Storage management, quality monitoring, governance
- Modeling: Dimensional modeling, star/snowflake schema
Core Competencies
- Design, develop, and optimize ETL workflows using Informatica PowerCenter (PC) and Informatica IDMC.
- Manage data ingestion processes from diverse data sources such as Salesforce, Oracle databases, PostgreSQL, and MySQL.
- Implement and maintain ETL processes and data pipelines to ensure efficient data extraction, transformation, and loading.
- Utilize Snowflake as the data warehouse solution for managing large volumes of structured and unstructured data.
- Maintain and optimize ETL jobs for performance and reliability, ensuring timely data availability for business users.
- Support data migration, data integration, and data consolidation efforts.
- Write and maintain basic Python scripts for data processing and automation tasks.
- Utilize Unix shell commands for data-related tasks and system management.
- Troubleshoot and resolve ETL-related issues, ensuring data integrity and availability.
- Ensure adherence to best practices for data governance and security.
Professional Experience
- Informatica Developer
- Developed ELT processes using PC/IDMC to integrate data into Snowflake.
- Implemented storage management for Azure Blob and Snowflake, enhancing data security.
- Worked on basis Python and shell scripting languages
Data Architecture Design :
- Design and develop scalable, efficient, and reliable data architectures that support data storage, processing, and analytics needs.
- Build and maintain data warehouses, databases, and other storage solutions.
- Create and optimize data models that meet the organization's analytical and business requirements.
Data Pipeline Development :
- Build and maintain ETL (Extract, Transform, Load) pipelines to move data from various sources to data warehouses, databases, or cloud storage systems.
- Ensure pipelines can handle high volumes of data, perform transformations, and ensure data integrity and accuracy.
- Automate data workflows and ensure they are scalable and robust for ongoing data processing tasks.
Data Integration :
- Integrate data from diverse sources (databases, APIs, third-party services, etc.) into unified systems.
- Collaborate with external teams to gather data requirements and develop integration solutions.
- Manage batch and real-time data integration processes, ensuring that data flows continuously and efficiently.
Data Quality Management :
- Ensure the accuracy, consistency, and reliability of data by implementing quality checks and monitoring.
- Identify and resolve issues related to missing, incomplete, or inconsistent data.
- Establish and maintain data governance standards to ensure compliance with regulatory and internal data policies.
Data Warehousing :
- Develop and optimize data warehouses, ensuring they are structured efficiently for query performance and analysis.
- Ensure data storage is cost-effective while providing fast access and scalability.
- Support the implementation of OLAP (Online Analytical Processing) and OLTP (Online Transaction Processing) solutions.
Performance Optimization :
- Optimize query performance to ensure fast data retrieval, using techniques like indexing, partitioning, and caching.
- Monitor and tune data systems for better performance, minimizing bottlenecks and downtime.
- Scale data infrastructure based on increasing data volumes and processing demands.
Cloud Data Engineering :
- Work with cloud platforms like AWS , Google Cloud Platform (GCP) , or Microsoft Azure to deploy, manage, and scale data storage and processing systems.
- Utilize cloud-native tools (e.g., AWS Redshift , BigQuery , Azure Synapse Analytics ) to manage and process data.
Collaboration with Data Teams :
- Work closely with data scientists and data analysts to understand their data requirements and provide clean, reliable, and accessible data.
- Support the development of machine learning models by providing clean, labeled, and pre-processed data.
- Collaborate with business intelligence teams to ensure data is structured in a way that supports reporting and analytics.
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