Data Engineer

Zencastr

13d

About The Position

As a Data Engineer, you will play a key role in building and maintaining the data systems that power analytics and reporting across the organization. This is a hands-on position where you will design and manage reliable data pipelines, transform raw operational data into analytics-ready datasets, and ensure teams have consistent access to trusted information. You will work cross-functionally to understand how data is generated and used, and translate those needs into scalable data models and structured reporting layers. Your focus will be on improving the reliability, structure, and accessibility of our data so that decision-making across the company is grounded in clear and well-defined metrics. This role is ideal for someone who enjoys owning data systems end to end, from ingestion and transformation through modeling and performance optimization, and who wants to help strengthen and mature our data foundation as the company grows.

Requirements

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

Responsibilities

Design, build, and maintain data pipelines and transformation workflows
Develop scalable data models to support analytics and operational reporting
Implement and manage our data warehouse and core data infrastructure
Improve data reliability, quality, and accessibility across systems
Establish foundational best practices for data modeling, documentation, and governance
Collaborate with analysts and business stakeholders to support evolving data needs
Monitor and optimize performance of pipelines and storage systems
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.