Google Data Engineer

Summary

Design, build, and maintain scalable data pipelines and warehouses on Google Cloud Platform using BigQuery, Dataflow, and Dataproc while ensuring data quality and collaborating with analysts.

Role: Google Data Engineer

Skill: sql,nosql,etl

Experience: 7 to 12 years

Location: Bangalore


Job Description:

  • Experience level of 3 to 5 years in data engineering, data warehousing, or a related field.
  • Experience with dashboarding tools like plx dashboard and looker studio ● Experience with building data pipelines, reports, best practices and frameworks.
  • Experience with design and development of scalable and actionable solutions (dashboards, automated collateral, web applications).
  • Experience with code refactoring for optimal performance.
  • Experience writing and maintaining ETLs which operate on a variety of structured and unstructured sources.
  • Familiarity with non-relational data storage systems (NoSQL and distributed database management systems).

Skills

  • Strong proficiency in SQL, NoSQL, ETL tools, BigQuery and at least one programming language (e.g., Python, Java).
  • Big Query,Data Flow,Data Proc,Cloud Sql,Teraform etc
  • Strong understanding of data structures, algorithms, and software design principles.
  • Experience with data modeling techniques and methodologies.
  • Proficiency in troubleshooting and debugging complex data-related issues.
  • Ability to work independently and as part of a team.

Responsibilities

  • Data Pipeline Development: Design, implement, and maintain robust and scalable data pipelines to extract, transform, and load data from various sources into our data warehouse or data lake.
  • Data Modeling and Warehousing: Collaborate with data scientists and analysts to design and implement data models that optimize query performance and support complex analytical workloads.
  • Cloud Infrastructure: Leverage Google Cloud and other internal storage platforms to build and manage scalable and cost-effective data storage and processing solutions.
  • Data Quality Assurance: Implement data quality checks and monitoring processes to ensure the accuracy, completeness, and consistency of data.

See also

Data Engineering jobs by country — openings, pay and top skills →

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