Data Engineer Jr (Databricks)

Taylors; Colonial Hills (SC); On-site; USD 90,000 - 130,000; Full time

Job summary

Taylors is seeking a Data Engineer to design, build, and run end-to-end data pipelines for North American clients. The role emphasizes strong foundational skills with internal opportunities to gain Databricks experience.

You will work on ingestion, transformation, and modeling pipelines, ensure data quality and reliability, and optimize performance and costs across data workloads. English communication with U.S.-based stakeholders is essential.

Qualifications

• Strong command of SQL and data modeling.
• Experience with Python or Scala.
• Solid understanding of data warehousing / lakehouse concepts.
• Experience working with cloud services (AWS / Azure / GCP).
• Conversational / professional English communication.

Responsibilities

• Design, build, and maintain data ingestion, transformation, and modeling pipelines.
• Implement best practices for data quality, observability, and reliability (testing, monitoring, SLAs, documentation).
• Optimize performance and costs in data workloads (partitioning, formats, tuning).
• Collaborate with analytics / data science teams to enable production-ready datasets.
• Participate in technical reviews, standard definition, and continuous delivery improvement.
• Communicate progress and risks clearly with stakeholders in English (clients and teams in the U.S.).

Skills

SQL

Python/Scala

Data modeling

Cloud services

English communication

Tools

Databricks

PostgreSQL

CI/CD

Job description

Language: Bilingual Spanish/English (mandatory; direct interaction with North American clients).
Industry / Stack: Data projects for U.S. clients; environment strongly aligned with Databricks / Lakehouse.

About the Role

We are looking for Data Engineer to build and run end-to-end data pipelines for North American clients. The priority is solid technical fundamentals; specific certification or training in emerging technologies (including Databricks) can be developed internally.

Responsibilities

• Design, build, and maintain data ingestion, transformation, and modeling pipelines (batch and, where applicable, streaming).
• Implement best practices for data quality, observability, and reliability (testing, monitoring, SLAs, documentation).
• Optimize performance and costs in data workloads (partitioning, formats, tuning).
• Collaborate with analytics / data science teams to enable production-ready datasets.
• Participate in technical reviews, standard definition, and continuous delivery improvement.
• Communicate progress and risks clearly with stakeholders in English (clients and teams in the U.S.).

Requirements (Must-Have)

• Strong command of SQL and data modeling; solid software engineering foundations (version control, code review, CI/CD preferred).
• Experience with Python or Scala (or another relevant data language).
• Solid understanding of data warehousing / lakehouse concepts, ETL/ELT, orchestration, and large-scale data management.
• Experience working with cloud services (AWS / Azure / GCP) or equivalent platforms.
• Conversational / professional English (meetings, technical reading and writing).

Nice-to-Have

• Experience with Databricks (Spark, Delta Lake, Lakehouse) or similar platforms.
• Knowledge of Postgres and OLTP concepts, and how they coexist with modern architectures (e.g., transactional vs. analytical needs).
• Familiarity with applied AI trends and/or AI agents (at the integration / infrastructure level, not necessarily advanced ML).
• Experience in consulting or delivery for international clients (requirements management, estimations, documentation).