Build robust data pipelines and dashboards that empower business teams with actionable insights, ensuring data quality and enabling data-informed decision-making.
Analytics at TRACTIAN
The Analytics team is responsible for building and maintaining from the data pipelines until the dashboards that feed up our customers and internal data products with relevant and actionable information. This department ensures reliable, trustworthy and meaningful data, merging them from several sources and delivering them in the edge efficiently. The team's core objective is to build reliable and insightful data products from the raw consumption of the data from different sources until the dashboards delivery to the consumer.
What you'll do
As an Analytics Engineer, you will build data products by designing, building, and maintaining robust data pipelines and dashboards that empower business teams with actionable insights. You will work closely with data analysts, data scientists, and business stakeholders to ensure data quality, optimize data workflows, and enable self-service analytics, driving data-informed decision-making across the organization.
Responsibilities
Develop and maintain scalable data pipelines and ETL processes to transform raw data into usable formats.
Design, implement, and optimize data models to support reporting and analytical needs.
Collaborate with business stakeholders to understand business requirements and translate them into technical specifications.
Ensure data quality and consistency across various sources by implementing data validation and cleansing techniques.
Work with cloud-based data warehouses and analytics platforms to manage and store large datasets.
Monitor and troubleshoot data pipelines to ensure reliable and timely delivery of data.
Document data processes, workflows, and best practices to enhance team knowledge and efficiency.
Create dashboards as data products as internal or external products
Requirements
B.S. in Computer Engineering, Data Science, Statistics, Computer Science, or a related field.
2+ years of experience in Data Science or Analytics.
Strong understanding in SQL for data modeling and experience with database management systems such as PostgreSQL and Clickhouse.
Strong understanding of data warehousing concepts and experience with ETL tools (e.g., Airflow, dbt).
Experience with BI tools (e.g., PowerBi, Looker, Tableau)
Experience with cloud-based data platforms like AWS Redshift.
Experience with programming languages such as Python.
Excellent communication skills, with the ability to convey complex technical concepts to non-technical stakeholders.
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