Senior Data Engineer (Remote)
Zencastr is hiring a hands-on Data Engineer to own its data systems end to end: building and maintaining pipelines and ingestion/streaming workflows, transforming raw operational data into analytics-ready datasets, and managing the warehouse and data models (SQL, ETL/ELT, dbt-style transformation layers) that power analytics and reporting across the company.
- Compensation
- Not disclosed
- Location
- San Francisco Office
- Work setup
- Remote
- Seniority
- Senior
- Employment
- Not specified
- Experience
- 5+ years
- Category
- Data engineering
- Posted
Skills
Must have
- 2–5 years of experience in data engineering, analytics engineering, or a closely related role
- Strong proficiency in SQL
- Experience with document-based operational databases (e.g., MongoDB)
- Experience with 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 with data ingestion or event streaming platforms (e.g., RudderStack, Segment, Kafka, Kinesis, Pub/Sub, or similar)
- Hands-on experience with modern data transformation and modeling frameworks (e.g., dbt, Dataform, or similar)
- 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, executing technically and contributing to evolving data architecture standards
Full description
About the Role
As our first dedicated Data Engineer, you will build and own the data foundation that powers analytics, reporting, and decision-making across the organization. This is a hands-on role where you'll design the dimensional model, own the pipelines that feed it, and establish the standards our data practice is built on.
You won't be starting from zero or working alone. You'll join a data team with direct analytics experience, and partner closely with engineering and other technical teams who have built and run what we have today. There's real institutional knowledge here to draw on. What's been missing is someone whose focus is turning it into a single, well-modeled foundation the whole company can rely on.
You will work cross-functionally to understand how data is generated and used, and translate those needs into scalable models and structured reporting layers. A central part of the work is identity resolution: building the spine that reliably connects the same entity as it appears across systems that each have their own identifiers and lifecycles.
This role is ideal for someone who enjoys owning data systems end to end — from ingestion and transformation through modeling, governance, and performance — and who wants the autonomy to design a warehouse properly, with colleagues who can help you understand the business behind the data.
What You’ll Do
- Design and build a conformed, Kimball-style dimensional model across our operational, behavioral, and transactional data
- Own ingestion end to end, including capturing change over time from sources that don't preserve history natively
- Consolidate transformation logic that currently lives in more than one place into a single governed, tested layer
- Implement and manage our data warehouse and transformation layer, taking ownership of the pipelines that move data from our operational systems into it
- Establish foundational best practices for data modeling, documentation, testing, and governance
- Improve data reliability, quality, and accessibility across systems
- Collaborate with analysts and business stakeholders to support evolving data needs
- Encode business metric definitions once, so that reporting stops drifting across teams
- Monitor and optimize performance and cost efficiency across pipelines, storage, and warehouse queries
You're a Good Fit If You
- Have 5+ years of experience specifically in data engineering, analytics engineering, or a closely related role, including having built and owned a dimensional model in production
- Have strong proficiency in SQL, with experience across document-based operational databases (e.g., MongoDB) and analytical data warehouses (e.g., BigQuery, Snowflake, Redshift, or similar)