Data Engineer

Role Overview

We are looking for a Data Engineer Semi Senior to join our data team working on high-impact financial digital products. Your mission will be to design, build, and optimize robust, secure, and scalable data pipelines that allow information to flow reliably within the organization and enable better business decisions.

What You Will Do

Design and develop efficient, secure, and scalable ETL and ELT pipelines, build data ingestion, transformation, processing, and loading processes, maintain and optimize data solutions developed on Snowflake, Apache Airflow, and relational databases, work with Python and SQL to process information, model data, and optimize queries, manage and optimize data storage and processing in AWS environments, integrate services like Amazon S3, AWS Glue, and Amazon Athena into the data platform, implement quality controls, validations, and automated testing on pipelines and transformations, incorporate monitoring, alerts, and observability mechanisms to detect failures, delays, or inconsistencies, resolve performance, availability, and data quality issues in production environments, apply good software engineering practices, including version control, code review, documentation, and CI/CD, collaborate with product, analytics, technology, and business teams to translate functional needs into technical solutions, and participate actively in agile ceremonies and contribute to the continuous improvement of processes and solutions.

Why It Might Be a Fit

The ideal candidate will have more than 5 years of experience in Data Engineering or similar positions, solid experience designing and building ETL or ELT pipelines, practical experience working with Snowflake in production environments, experience developing and maintaining DAGs and pipelines with Apache Airflow, advanced knowledge of Python applied to data engineering, advanced knowledge of SQL, including modeling, transformation, and optimization of queries, experience with relational databases, preferably PostgreSQL, experience working with AWS and services like Amazon S3, AWS Glue, and Amazon Athena, knowledge of cloud infrastructure, including IAM, EC2, or EKS, experience with Git, GitLab, or equivalent version control tools, experience participating in CI/CD pipelines, knowledge or experience with automated testing applied to data pipelines, ability to work with technical and business teams, autonomy to resolve medium-complexity problems and propose improvements, familiarity with agile methodologies like Scrum or Kanban, and experience implementing quality and observability data solutions.

Requirements

• More than 5 years of experience in Data Engineering or similar positions
• Solid experience designing and building ETL or ELT pipelines
• Practical experience working with Snowflake in production environments
• Experience developing and maintaining DAGs and pipelines with Apache Airflow
• Advanced knowledge of Python applied to data engineering
• Advanced knowledge of SQL, including modeling, transformation, and optimization of queries
• Experience with relational databases, preferably PostgreSQL
• Experience working with AWS and services like Amazon S3, AWS Glue, and Amazon Athena
• Knowledge of cloud infrastructure, including IAM, EC2, or EKS
• Experience with Git, GitLab, or equivalent version control tools
• Experience participating in CI/CD pipelines
• Knowledge or experience with automated testing applied to data pipelines
• Ability to work with technical and business teams
• Autonomy to resolve medium-complexity problems and propose improvements
• Familiarity with agile methodologies like Scrum or Kanban
• Experience implementing quality and observability data solutions