Data Engineer

Overview

Who is this for

If building scalable data infrastructure excites you, this is the place to be. Fornax is a team of cross-functional individuals who solve critical business challenges using analytics and innovative data solutions.
We are seeking a skilled Data Engineer to work on our cutting-edge data product.

The ideal candidate will possess strong technical expertise in modern data stack technologies and a passion for building robust, scalable data platforms.

The Data Engineer will play a critical role in architecting, developing, and maintaining our data product infrastructure. This role involves working closely with data scientists, analytics engineers, and product stakeholders to build high-performance data pipelines, optimize query performance, and deliver reliable data solutions. The ideal candidate has a strong background in data engineering, distributed systems, and modern data tooling.

Key Responsibilities

Data Infrastructure & Pipeline Development (50%)

• Design, build, and maintain scalable data pipelines using Prefect for orchestration and workflow management
• Implement ELT processes using Dlthub to efficiently load data from various sources into the data platform
• Develop and optimize data transformation workflows using DBT to ensure clean, modeled, and business-ready datasets
• Build and manage Apache Iceberg table formats to enable efficient data lakehouse operations with ACID transactions
• Leverage DuckDB for fast local analytics, development testing, and embedded analytical workloads
• Ensure data pipeline reliability, monitoring, and error handling with comprehensive logging and alerting mechanisms

Query Optimization & Performance Engineering (20%)

• Design and optimize distributed query execution using Trino for high-performance analytics across diverse data sources
• Utilize DuckDB for rapid prototyping, local query testing, and in-process analytical operations
• Implement query optimization strategies including partition pruning, predicate pushdown, and materialized views
• Monitor and tune query performance to ensure sub-second response times for critical business queries
• Develop best practices for efficient data access patterns and resource utilization across different query engines

Data Modeling & Architecture (15%)

• Implement semantic layer and metrics definitions for consistent business logic across applications
• Design dimensional models and data mart architectures to support analytics and reporting use cases
• Collaborate with analytics engineers and stakeholders to translate analytical requirements into optimized data structures
• Establish and maintain data modeling standards and documentation for the data product

Data Quality & Governance (10%)

• Implement data quality frameworks and validation checks within DBT models and Prefect workflows
• Develop automated data testing and monitoring solutions to ensure data accuracy and consistency
• Document data lineage, schema definitions, and transformation logic to maintain data governance standards
• Establish SLAs for data freshness, quality, and pipeline reliability

Collaboration & Product Development (5%)

• Work closely with product managers, data scientists, and business stakeholders to understand data product requirements
• Participate in technical design reviews and contribute to architectural decisions
• Provide technical guidance and mentorship to junior team members
• Stay current with emerging technologies and best practices in the data engineering ecosystem