Data Engineering Lead

hace 2 días


Buenos Aires, Buenos Aires C.F., Argentina TASQ Staffing Solutions A tiempo completo

We are seeking a Data Engineering Lead at TASQ Staffing Solutions in Makati or Iloilo.

The estimated salary for this role is ₱1,200,000 - ₱1,500,000 per year, depending on experience.

Job Description

As a Data Engineering Lead, you will directly report to the Head of Data and Analytics and be responsible for designing and developing data modeling, data pipelines supporting our platform and apps, as well as providing data and reporting support for business end-users.

Key Responsibilities
  • Design and development of reporting data models and data transformation jobs, including the modeling of very large data sets.
  • Identify and implement the most efficient ways of performing data transformation tasks using best practice methods and tooling.
  • Prepare and maintain documentation such as business requirements documents, design specifications, and test cases.
  • Work with stakeholders (including data team, software engineers, and product team) to understand business requirements and translate them into technical specifications.
  • Lead the data migration and modeling process from GCP to data warehouse.
  • Responsible for data warehouse administration, user access, and security.
  • Contribute to the design and implementation of our data model and ETL framework.
Requirements

We are looking for a highly experienced Data Engineer with a minimum of 2+ years of experience in a data engineering environment, with hands-on experience building and maintaining complex data environments in the cloud (preferably GCP BigQuery and/or Snowflake).

You should have extensive experience with SQL (Postgres preferred), with a core focus on analyzing and validating complex and disparate data sets to find gaps between datasets, requirements, and source systems.

Demonstrate understanding and experience with following data engineering competencies:

  • Data warehousing principles, including data architecture, modeling, database design, and performance optimization best practices.

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