About
Experience Required - 6+ Years
Must Have Technical/Functional Skills
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Strong hands-on experience in Big Data technologies and distributed data processing frameworks.
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Proficiency in Python for data engineering, scripting, and performance optimization.
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Extensive experience with Apache Spark (PySpark/Spark SQL) for large-scale data transformation and processing.
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Solid experience in designing and developing ETL/ELT pipelines for batch and real-time data processing.
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Experience working with Google Cloud Platform (GCP) services such as BigQuery, Cloud Storage, Dataflow, Dataproc, Composer, and Pub/Sub.
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Strong understanding of data warehousing concepts, dimensional modeling, and data lake architectures.
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Experience with CI/CD pipelines and DevOps practices for data engineering workflows.
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Proficiency in version control systems (Git) and build/release management.
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Experience with workflow orchestration tools such as Apache Airflow or Cloud Composer.
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Experience working in Agile/Scrum environments with cross-functional teams.
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Strong analytical and problem-solving skills
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Strong collaboration and stakeholder communication skills
Roles & Responsibilities
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Design, develop, and maintain scalable Big Data pipelines using Spark (PySpark/Spark SQL) for batch and real-time processing.
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Build and optimize robust ETL/ELT workflows to ingest, transform, and load data from multiple structured and unstructured sources.
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Develop high-performance SQL queries and implement efficient data models to support analytics and reporting needs.
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Architect and implement data solutions on Google Cloud Platform (GCP) using services such as BigQuery, Dataproc, Dataflow, Cloud Storage, Pub/Sub, and Composer.
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Monitor, troubleshoot, and resolve production issues in distributed and cloud environments.
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Automate operational processes and enforce coding standards, version control, and documentation practices.
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Automate operational processes and enforce coding standards, version control, and documentation practices.
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Collaborate with cross-functional teams including business stakeholders, data engineers, and product teams
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Drive analytics initiatives to provide actionable insights.
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Participate in Agile ceremonies including sprint planning, design reviews, and retrospectives.
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Continuously improve reporting frameworks and data processes in alignment with Agile delivery practices
Languages
- English
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