Chief Data Architecture Specialist

1 day ago

Bengaluru, Karnataka, India beBeeDataEngineeringLeader Full time ₹ 1,04,000 - ₹ 1,30,878
Job Title:
Data Engineering Leader

As a Data Engineering Leader, you will design, develop, and optimize data pipelines, ETL processes, and data integration solutions using Python, Spark, SQL, Snowflake, dbt, and other relevant technologies.


Requirements
  • A bachelor's degree in Computer Science, Engineering, Operations Management, or a related field is required. A Master's degree is preferred.
  • 5+ years of experience in data engineering, with proficiency in Python, Spark, SQL, Snowflake, dbt, and other relevant technologies.
  • Strong domain expertise in operations organizations, particularly in functions like supply chain and manufacturing.
  • Experience with big data processing frameworks such as Apache Spark and cloud-based data platforms such as Snowflake.
  • Familiarity with dbt (Data Build Tool) for managing data transformation and modeling workflows.
  • Good understanding of advanced data engineering and data science practices and technologies like pypark, sagemaker, cloudera MLflow etc.
  • Excellent problem-solving skills, analytical thinking, and attention to detail.
  • Strong communication and interpersonal skills, with the ability to collaborate effectively with cross-functional teams and operations stakeholders.

Responsibilities
  1. Lead the design, development, and optimization of data pipelines, ETL processes, and data integration solutions using Python, Spark, SQL, Snowflake, dbt, and other relevant technologies.
  2. Apply strong domain expertise in operations organizations, particularly in functions like supply chain and manufacturing, to understand data requirements and deliver tailored solutions.
  3. Utilize big data processing frameworks such as Apache Spark to process and analyze large volumes of operational data efficiently.
  4. Implement data transformations, aggregations, and business logic to support analytics, reporting, and operational decision-making.
  5. Leverage cloud-based data platforms such as Snowflake to store and manage structured and semi-structured operational data at scale.
  6. Utilize dbt (Data Build Tool) for data modeling, transformation, and documentation to ensure data consistency, quality, and integrity.
  7. Monitor and optimize data pipelines and ETL processes for performance, scalability, and reliability in operations contexts.
  8. Conduct data profiling, cleansing, and validation to ensure data quality and integrity across different operational data sets.
  9. Collaborate closely with cross-functional teams, including operations stakeholders, data scientists, and business analysts, to understand operational challenges and deliver actionable insights.
  10. Stay updated on emerging technologies and best practices in data engineering and operations management, contributing to continuous improvement and innovation within the organization.

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