Data Software Engineer

Hybrid in Ecuador: Quito

Data Software Engineering

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We are looking for a skilled Data Software Engineer to join our team. In this role, you will design, build, and deploy robust and efficient data products while following development best practices and delivering within agreed timelines. You will work closely with a Senior Data Engineer who will support and guide your growth, and you'll play an active part in shaping high-quality data solutions aligned with our Data Architecture vision.

The ideal candidate combines strong technical expertise in databases and data processing with well-developed soft skills, including goal orientation, teamwork, effective communication, accountability, and planning.

Responsibilities

Analyze requirements based on the definitions and designs provided
Participate in the engineering design phase, applying high efficiency standards aligned with the Data Architecture vision
Develop and deploy data products such as ETLs, ELTs, and APIs that make information available, following company standards, approved tools, and development best practices (e.g., Clean Code) to ensure solutions that are efficient (in terms of resource consumption and response time), effective, readable, and easy to maintain over time
Apply testing strategies to verify the correct implementation of both functional and non-functional criteria
Drive continuous improvement processes for the products under development
Actively participate in pair programming to identify improvement opportunities in your colleagues' work
Resolve incidents promptly and propose long-term solutions to prevent recurrence
Manage the enablers required for your development work, with the support and supervision of the assigned Senior Data Engineer
Document the data products you build according to company standards and platforms

Requirements

3+ years of experience in a similar role
Strong database management skills with proven experience optimizing scripts, stored procedures, and other data processing objects
Experience modeling both relational and non-relational databases, including relational engines such as Oracle, SQL, and PostgreSQL, and NoSQL databases such as MongoDB, Cassandra, Elasticsearch, and Neo4J
Hands-on experience building Data Warehouses, Data Marts, and Data Lakes
Experience building ETLs and ELTs, and managing large data volumes, using on-premise tools such as SQL Server Integration Services (SSIS), Pentaho (Kettle), or SAP Data Services, as well as cloud pipeline tools such as MS Azure Data Factory, AWS Glue, or Dataflow
Familiarity with major cloud platforms (AWS, Google Cloud Platform, Microsoft Azure) and their components in Big Data solutions
Experience with distributed file systems such as Hadoop, HDFS, or Spark, and with batch processing using Big Data technologies
Proficiency with Apache Spark (SQL and Python) and PySpark
Experience with version control tools
Solid understanding of development best practices and principles, such as Clean Code
Knowledge of Agile frameworks and DataOps strategies
Experience with data-oriented testing strategies for both functional and non-functional testing
Strong soft skills, including goal orientation, critical thinking, teamwork, effective communication, accountability, and planning
Ability to explain technical concepts in terms understandable to non-technical audiences
Proactive approach to resolving blockers

Nice to have

Knowledge of the banking and financial sector
AI certification at any level (preferably Copilot; Claude, Gemini, GPT, Grok, and others are a plus)