We are looking for a skilled and motivated Data Engineer Specialist to join our team. The responsibilities of this role is to design, build, and maintain a robust, self-service, scalable, and secure data platform and end-to-end data pipelines that empowers Data Analysts, and Data Scientists to deliver insights and drive strategic decision-making.
Responsibilities of a Data Engineer at QuintoAndar:
Build and maintain a high-performance data platform that meets the company's needs, connects with product solutions, and leads analytical innovation, enabling incredible architectures and efficient platforms;
Create and edit data pipelines, considering business logic that best applies to the area in question, choosing levels of aggregation, grouping and transforming fields, checking data quality, and cleaning the data;
Create data modeling and transformation workflows, enabling the creation of clear and accessible data abstractions;
Responsible for the entire code development lifecycle (monitoring deployment, documentation, performance, security, adding metrics and alarms, ensuring SLO budget compliance, and more);
Investigate inconsistencies and be able to trace the source of differences (data troubleshooting);
Enable teams across the company to access and use data more effectively through self-service tools and well-modeled datasets;
Align with stakeholders to understand their primary needs, while also having a holistic view of the problem and proposing extensible, scalable, and incremental solutions;
Conduct PoCs and benchmarks to determine the best tool for a given problem, and decide whether to use an off-the-shelf solution or develop one in-house;
Contribute to defining the strategic vision, crossing team and service boundaries to solve problems;
Advocate for the value of data analytics and engineering within the organization and fostering a data-driven culture;
Be a reference within the chapter on technical concepts, tools, and/or best coding practices.
What we are looking for:
Specialist in technologies, solutions, and concepts of Big Data (Spark, Hadoop, Hive, MapReduce) and multiple languages (YAML, Python);
Experience with Airflow, Spark, AWS and Databricks;
Strong foundation in software engineering principles, with experience working on data-centric systems;