Data Engineer Manager - AWS Databricks

New
J
JobgetherData Engineering
IndiaFull-TimeManager
Salary not disclosed
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Job Details

Experience
7+ years
Required Skills
AWSCloud ComputingGitData engineeringData modelingDatabricksGitHub

Requirements

  • 7+ years of professional experience in data engineering.
  • Demonstrated experience leading or mentoring data engineering teams.
  • Hands-on expertise developing, maintaining, and optimizing data pipelines in Databricks.
  • Strong experience with GitHub repositories, Git workflows, and version control.
  • Proven ability to refactor and maintain legacy codebases.
  • Strong understanding of data modeling and the design of reusable data components.
  • Experience building reliable, scalable data platforms with a focus on data quality and governance.
  • Ability to translate business requirements into technical solutions.
  • Strong problem-solving, communication, and technical leadership skills.
  • Experience with AWS and cloud-based data engineering environments.

Responsibilities

  • Lead, mentor, and support a team of data engineers while remaining actively involved in development, architecture, code reviews, and technical decision-making.
  • Define and execute the technical strategy for Journey Analytics data platforms.
  • Design, build, and maintain scalable, automated data pipelines using Databricks.
  • Develop modular and reusable data components to reduce duplication and improve maintainability.
  • Design and evolve scalable data models for analytics and reporting.
  • Manage and optimize GitHub repositories, promoting strong version-control practices and development workflows.
  • Lead and participate in refactoring legacy codebases to improve scalability, performance, and reusability.
  • Establish standards for data quality, governance, performance, and reliability.
  • Collaborate with analytics, product, and engineering stakeholders to align technical solutions with business priorities.
  • Identify technical risks and drive continuous improvement across data processes and automation.
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