Data Engineer

Zencastr is seeking a Data Engineer to build and maintain the data systems that support analytics and reporting across the organization. The role focuses on developing reliable pipelines, scalable data models, ingestion and transformation workflows, and trusted analytics-ready datasets while partnering with cross-functional stakeholders.

Responsibilities:

• Design, build, and maintain reliable data pipelines that transform operational data into structured, analytics-ready datasets
• Manage and optimize data ingestion and event streaming workflows to ensure consistent, high-quality upstream data flows
• Develop and maintain scalable data models and data marts to support reporting and business analysis
• Implement and manage transformation processes that structure raw data for analytics use
• Ensure strong standards for data quality, validation, and consistency across systems
• Monitor and optimize performance, reliability, and cost efficiency within the analytics environment
• Partner cross-functionally to translate business requirements into scalable data solutions
• Proactively improve our data systems to ensure they remain structured, consistent, and scalable as the organization grows

Requirements:

• Have 2–5 years of experience in data engineering, analytics engineering, or a closely related role
• Strong proficiency in SQL, with experience working across document-based operational databases (e.g., MongoDB) and analytical data warehouses (e.g., BigQuery, Redshift, PostgreSQL, Snowflake, or similar)
• Experience building and maintaining reliable ETL/ELT pipelines that transform raw application data into structured, analytics-ready datasets
• Experience working with data ingestion or event streaming platforms (e.g., RudderStack, Segment, Kafka, Kinesis, Pub/Sub, or similar) and ensuring consistent, reliable upstream data flows
• Hands-on experience with modern data transformation and modeling frameworks (e.g., dbt, Dataform, or similar), including managing transformation layers within a warehouse environment
• Experience building fact and dimension tables using star schema principles to support reporting and data marts
• Solid understanding of data modeling best practices, including schema design, dimensional modeling, and performance considerations
• Strong focus on data quality, validation, and governance, with the ability to identify and resolve data inconsistencies
• Understanding of performance optimization across pipelines, storage, and warehouse queries
• Comfortable operating in a growing environment where you can both execute technically and contribute to evolving data architecture standards