Offer summary

Qualifications:

2+ years of experience in analytics engineering, Proficient in Data warehousing and PostgreSQL, Bachelor's degree in Computer Science or Engineering, Native or fully fluent in English.

Key responsabilities:

Design, create, and maintain data models

Manage ELT pipelines for reporting systems from Redshift warehouse

Job description

This is a remote position.

We are seeking a top candidate to join our team as Analytics Engineer.

Compensation:
USD 2.3K - 2.64K/month.

Location:
Remote (anywhere).

Mission of Softgic:
In Softgic S.A.S. we work for the digital and cognitive transformation of our clients, aware that quality is an essential factor for us, we incorporate the following principles into our policy:

Deliver quality products and services.
Achieve the satisfaction of our internal and external clients.
Encourage in our team the importance of training to grow professionally and personally through development plans.
Comply with the applicable legal and regulatory requirements.
Promote continuous improvement of the quality management system.

What makes you a strong candidate:

You have 2+ years of experience in analytics engineering, and data modeling
You are proficient in Data warehousing, gestión de pipelines ELT, PostgreSQL, and software testing.
English - Native or fully fluent.

Responsibilities and more:

Location: Remote

Shift: Full-time

Education: Bachelor's degree in Computer Science, Engineering, or related field

English Level: C1-C2

Schedule: UK time, the candidate must be willing to work in that time zone.

Job Description:

As an Analytics Engineer, you will develop and maintain data models that enhance analyst productivity, empower machine learning models, and support self-service data discovery throughout the company.

Responsibilities:

Design, create, and maintain data models in our Redshift data warehouse.
Manage ELT pipelines feeding reporting systems and analytical teams.
Apply software engineering best practices (e.g., testing, version control) to our data environment.
Collaborate with business and engineering teams to build a scalable, reliable, and robust data platform architecture.
Ensure that application designs support reporting and analytics requirements.
Support product analysts with automation, tooling, data pipelines, and data engineering expertise.