Data Engineer

Zencastr; United States; Remote; USD 110,000 - 150,000; Full time

Job summary

Zencastr is seeking a Data Engineer to design, build, and maintain data pipelines and transformative data models that power analytics across the organization.

You will manage data ingestion, transformation, and reporting layers to ensure reliable, analytics-ready information for stakeholders across teams.

Qualifications

• 2–5 years of data engineering or analytics engineering experience.

Responsibilities

• Design, build, and maintain data pipelines and transformation workflows.

Skills

SQL

MongoDB

Data modeling

ETL/ELT pipelines

Data warehousing

dbt / Dataform

Streaming data

Performance tuning

Tools

BigQuery

Redshift

Snowflake

PostgreSQL

Kafka

Kinesis

Pub/Sub

RudderStack

Segment

dbt

Dataform

Job description

About the Role

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.

What You’ll Do

• 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

You’re a Good Fit If You

• 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

Key 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.