Senior GCP Data Engineer

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Requirements:

• 8+ years of Strong experience in ETL and ELT data from various sources in Data Warehouses.
• 8+ years of experience in Python, Pandas, Numpy, and SciPy.
• 5+ years of Experience in GCP.
• 5+ years of Experience in BigQuery, PySpark, and Pub/Sub.
• 5+ years of Experience working with and creating data architectures.
• Certified in Google Cloud Professional Data Engineer.
• Advanced proficiency in Google Cloud services such as Dataflow, Dataproc, Dataprep, Data Studio, and Cloud Composer.
• Proficient in writing complex Spark (PySpark) User Defined Functions (UDFs), Spark SQL, and HiveQL.
• Good understanding of Elastic search.
• Experience in assessing and ensuring data quality, data testing, and addressing data quality issues.
• Excellent understanding of Spark architecture and underlying frameworks including storage management.
• Solid background in database design and development, database administration, and software engineering across full life cycles.
• Experience with NoSQL data stores like MongoDB, DocumentDB, and DynamoDB.
• Knowledge of data governance principles and practices, including data lineage, metadata management, and access control mechanisms.
• Experience in implementing and optimizing data security controls, encryption, and compliance measures in GCP environments.
• Ability to troubleshoot complex issues, perform root cause analysis, and implement effective solutions in a timely manner.
• Proficiency in data visualization tools such as Tableau, Looker, or Data Studio to create insightful dashboards and reports for business users.
• Strong communication and interpersonal skills to effectively collaborate with technical and non-technical stakeholders, articulate complex concepts, and drive consensus.
• Experience with agile methodologies and project management tools like Jira or Asana for sprint planning, backlog grooming, and task tracking.

Responsibilities:

• Collaborate with cross-functional teams to define, prioritize, and execute data engineering initiatives aligned with business objectives.
• Design and implement scalable, reliable, and secure data solutions by industry best practices and compliance requirements.
• Drive the adoption of cloud-native technologies and architectural patterns to optimize performance, cost, and reliability of data pipelines and analytics solutions.
• Mentor and lead a team of Data Engineers.
• Demonstrate a drive to learn and master new technologies and techniques.
• Apply strong problem-solving skills with an emphasis on building data-driven or AI-enhanced products.
• Coordinate with ML/AI and engineering teams to understand data requirements.