Description review

Data Warehouse Engineer

Tires-Easy · Argentina · back to the listing

HR standards

58/100

needs work

Title ↔ description

90/100

strong

Reads as

Data Engineer

100% confident

How others title the same work

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What the listing never says

  • 19 bullet points. Long requirement lists deter qualified candidates, who read them as hard gates. Scope clarity
  • No pay range published. Candidates cannot tell whether applying is worth their time. Pay transparency

The listing, marked up

ABOUT US:

We’re on a mission to change the tire industry — how it’s delivered and how it’s experienced. A pioneer in the automotive e-commerce industry, we take pride in our extensive selection of top-name brands and budget-friendly options, ensuring our customers find the perfect tire to suit their needs. As a part of our growing and dynamic team, you'll contribute to our commitment to exceptional service, innovation, and customer satisfaction. Embrace the opportunity to be a driving force behind one of the fastest growing e-commerce companies in the US and apply now1 !

SUMMARY:

We are seeking a skilled and detail-oriented Data Warehouse Engineer to join our Business Intelligence team and play a key role in designing, building, and optimizing our data infrastructure. In this role, you will develop and maintain scalable data pipelines, ensure the accuracy and performance of our warehouse systems, and collaborate closely with analysts, engineers, and stakeholders to support data-driven decision-making across the organization. You will be instrumental in driving data quality, reliability, and efficiency, while fostering a culture of technical excellence and continuous improvement.

As a member of our team, you will work in an entrepreneurial environment where we are constantly looking to learn what our customers need and to develop better ways to serve them.

Resume must be submitted in English.

ESSENTIAL DUTIES AND RESPONSIBILITIES:

• Design, build, and maintain scalable, high-performance data warehouse solutions using modern cloud and on-premise technologies.

• Develop, schedule, and monitor robust ETL/ELT pipelines using tools such as Apache Airflow for orchestration and dbt for data transformation.

• Optimize data models, queries, and storage strategies to improve performance, scalability, and system reliability.

• Implement and enforce data quality, validation, and governance standards to ensure accuracy, consistency, and compliance.

• Collaborate with data analysts, engineers, and business stakeholders to translate analytical requirements into efficient data solutions.

• Continuously enhance and automate data workflows by leveraging Airflow, dbt and other modern frameworks to drive efficiency and continuous improvement.

• AWS Linux controls for DW setup

QUALIFICATION REQUIREMENTS

• Bachelor’s degree in Computer Science, Information Systems, Data Engineering, or a related technical field (Master’s preferred).

• 3+ years of experience designing, developing, and maintaining data warehouses and ETL/ELT workflows in production environments.

• Proficiency in SQL and strong experience with data modeling, schema design, and performance optimization.

• Proven hands-on experience with modern data engineering tools (e.g. Airflow, dbt) and AWS cloud-based data platforms (e.g., Snowflake, Redshift).

• Hands-on experience (on business projects) with programming languages such as: Python, C/C++/Java.

• Strong analytical and problem-solving skills, with the ability to work collaboratively across cross-functional teams in a fast-paced, data-driven environment.

• Must not have had any disciplinary actions regarding attendance, conduct and/or performance in the past 90 days, and must not be approaching such action.

DESIRED SKILLS

• Experience using: Excel, Google Sheets, Slack

• AirFlow, dbt, Linux, PostGre

• AWS cloud practitioner or engineer certificates are a plus

• High speed Wi-Fi connection

• Be able to work in US timezone shifts (e.g., EST, CST, MST, or PST).

Originally posted on Himalayas

How this was produced

Highlights are found by rule, not by a model: each one is a phrase matched at a known position, and every note is a template we wrote. The two scores come from a typed-decision model (Jev) that reads the listing against the official role definition and real listings for the same role, and returns probabilities rather than prose — it never writes any of the words on this page, and never chooses what to highlight.

Deterministic penalty applied to the HR score: 9 points (from 67 before penalties). Reviewed 21 Sep 2026.