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
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