Chief Data Architect

2 weeks ago

alappuzha, India beBeeData Full time

Job Title: Senior Data EngineerWe are seeking a skilled Senior Data Engineer to join our team. The ideal candidate will have strong experience with cloud platforms, modern ETL/ELT tools, and deep technical skills in Python, SQL, and distributed data frameworks.Key Responsibilities:Design and Develop Scalable Data Pipelines: Design, develop, and maintain scalable and reliable ETL/ELT pipelines that efficiently process large-scale structured and unstructured datasets.Build Data Models and Warehouses: Build data models, data marts, and data warehouses to support analytics and reporting needs.Work with Large-Scale Datasets: Work with large-scale structured and unstructured datasets across multiple sources.Implement Quality Frameworks: Implement data quality, validation, and monitoring frameworks to ensure high-quality data processing.Optimize Existing Pipelines: Optimize existing pipelines for better performance, cost efficiency, and maintainability.Collaborate with Teams: Collaborate with Data Science, BI, Product, and Engineering teams to gather and implement data requirements.Write Optimized Code: Write optimized SQL queries and well-structured, maintainable Python code.Support Production Pipelines: Support production pipelines by troubleshooting issues and performing root cause analysis.Use Version Control and CI/CD Tools: Use version control, CI/CD, and automated deployment tools for data engineering tasks.Ensure Compliance: Ensure compliance with data security, privacy, and governance standards.Required Skills & Qualifications:5+ Years of Experience: 5+ years of hands-on experience in Data Engineering.Programming Skills: Strong programming skills in Python and SQL.ETL Frameworks: Experience with ETL frameworks (Airflow, dbt, Spark, Glue, Dataflow, etc.).Cloud Platforms: Expertise in cloud data platforms (AWS, Azure, or GCP).Data Modelling: Strong understanding of data modelling and data warehouse/lakehouse architectures.Big Data Tools: Experience working with big data tools (Spark, Kafka, Hadoop) is a plus.Source Control: Experience with source control (Git) and CI/CD pipelines.Benefits:Opportunity to Work with Big Data: Opportunity to work with large-scale structured and unstructured datasets.Collaborative Environment: Collaborative environment with Data Science, BI, Product, and Engineering teams.Professional Development: Opportunities for professional development and growth.Others:Preferred / Nice-to-Have Skills: Experience with Snowflake, Redshift, BigQuery, Databricks, or Synapse.Exposure to Event-Driven Architectures: Exposure to event-driven or real-time data streaming architectures.ML Pipeline Integration: Experience integrating with ML pipelines or supporting Data Science workloads.Containerization: Familiarity with containerization (Docker, Kubernetes).

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