Data Infrastructure Architect

7 days ago

Lahore, Punjab, Pakistan beBee Careers Full time
Job Description

This role is critical to powering analytics, automation, and AI initiatives.

Key Responsibilities:
  • Architect and Build Systems: Design and lead the implementation of scalable, cloud-based data pipelines and infrastructure.
  • Manage Cloud Environments: Own and operate data infrastructure on cloud platforms—preferably Google Cloud Platform (GCP).
  • Develop Pipelines: Build and manage data pipelines from first-party and third-party sources, ensuring data integrity and performance.
  • Support MLOps: Enable machine learning workflows through reliable data ingestion, monitoring, and serving pipelines.
  • Automate Deployments: Set up and maintain CI/CD pipelines for smooth and efficient delivery of data products.
  • Collaborate Across Functions: Work closely with engineers, analysts, and product teams to ensure data is accurate, accessible, and aligned with business goals.
  • Optimize Warehousing: Administer and fine-tune cloud data warehouses (e.g., BigQuery, Redshift, Snowflake) for both real-time and batch workloads.
  • Oversee Integrations: Coordinate with third-party software and data providers for seamless integration and maintenance.
  • Lead and Mentor: Provide technical leadership to team members and contribute to a strong, knowledge-sharing engineering culture.
  • Innovate Constantly: Evaluate and introduce new tools, frameworks, and workflows to improve data systems and business outcomes.
  • Own the Lifecycle: Drive projects from initial concept to deployment, scaling, and long-term support.
Requirements:
  • 10+ years of experience in data engineering and cloud infrastructure, with 2+ years in a senior or lead role.
  • Strong expertise in cloud architecture, especially on GCP; experience with AWS or Azure is a plus.
  • Advanced proficiency in SQL and Python for data transformation and automation.
  • Hands-on experience with cloud data warehouses like BigQuery, Snowflake, or Redshift.
  • Proven ability to build and manage scalable pipelines for structured and unstructured data.
  • Solid understanding of MLOps workflows and tools.
  • Experience with CI/CD practices in data environments.
  • Strong communication and collaboration skills.
  • Ability to thrive in fast-paced, evolving environments and drive clarity in ambiguity.
  • Familiarity with marketing and eCommerce data sources (e.g., APIs from Google Analytics, Meta Ads, Shopify, TikTok, etc.).

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