Senior Data Engineer

---

Miami, Florida  

Salary: $155,000 - $185,000 + bonus + equity

Overview

We are supporting a high-growth software company building enterprise-grade data platforms that power real-time analytics, product intelligence, and AI-driven decision making. This role is designed for a senior data engineer who enjoys owning pipeline architecture, shaping scalable data ecosystems, and enabling engineering teams to move faster through reliable infrastructure. You will work within a modern cloud environment where data is treated as a production asset, not an afterthought.

Key Responsibilities

• Design and maintain scalable ETL/ELT pipelines for structured and unstructured data
• Build real-time and batch processing frameworks supporting analytics and product teams
• Own data modelling standards to optimise performance and usability
• Partner with platform and backend engineers to improve data availability
• Implement data quality, governance, and observability practices
• Optimise storage and compute costs across cloud environments

Tech Stack

• Languages: Python, SQL
• Cloud: AWS (S3, Lambda, Redshift) or GCP (BigQuery)
• Processing: Spark, Kafka, or Flink
• Orchestration: Airflow, Prefect, or Dagster
• Warehousing: Snowflake, BigQuery, or Redshift
• Infrastructure: Terraform

Ideal Background

• 5+ years building production data systems
• Strong expertise in distributed data processing
• Proven experience designing high-volume pipelines
• Comfortable working in product-driven engineering teams
• Strong stakeholder communication across technical and non-technical groups

Why Join

• Own critical data infrastructure within a scaling software business
• Direct influence on product intelligence and AI readiness
• Engineering-led culture with minimal bureaucracy
• Competitive compensation with meaningful equity
• Hybrid flexibility

Miami, Florida  

Salary: $155,000 - $185,000 + bonus + equity

Overview

We are supporting a high-growth software company building enterprise-grade data platforms that power real-time analytics, product intelligence, and AI-driven decision making. This role is designed for a senior data engineer who enjoys owning pipeline architecture, shaping scalable data ecosystems, and enabling engineering teams to move faster through reliable infrastructure. You will work within a modern cloud environment where data is treated as a production asset, not an afterthought.

Key Responsibilities

• Design and maintain scalable ETL/ELT pipelines for structured and unstructured data
• Build real-time and batch processing frameworks supporting analytics and product teams
• Own data modelling standards to optimise performance and usability
• Partner with platform and backend engineers to improve data availability
• Implement data quality, governance, and observability practices
• Optimise storage and compute costs across cloud environments

Tech Stack

• Languages: Python, SQL
• Cloud: AWS (S3, Lambda, Redshift) or GCP (BigQuery)
• Processing: Spark, Kafka, or Flink
• Orchestration: Airflow, Prefect, or Dagster
• Warehousing: Snowflake, BigQuery, or Redshift
• Infrastructure: Terraform

Ideal Background

• 5+ years building production data systems
• Strong expertise in distributed data processing
• Proven experience designing high-volume pipelines
• Comfortable working in product-driven engineering teams
• Strong stakeholder communication across technical and non-technical groups

Why Join

• Own critical data infrastructure within a scaling software business
• Direct influence on product intelligence and AI readiness
• Engineering-led culture with minimal bureaucracy
• Competitive compensation with meaningful equity
• Hybrid flexibility

San Francisco, California  

Salary: $175,000 - $220,000 + equity 

Overview

We are partnered with an AI-native software company developing production-grade large language model applications used by millions of users globally. This role is built for a technically fluent prompt engineer who understands that prompting is not experimentation, it is system design. You will sit at the intersection of product, machine learning, and applied research, shaping how users interact with intelligent systems at scale.

Key Responsibilities

• Design, test, and optimise prompt architectures for production LLM systems
• Build evaluation frameworks to measure output quality and model behaviour
• Partner with ML engineers to improve model performance through structured prompting
• Translate ambiguous product requirements into deterministic AI workflows
• Develop guardrails that improve reliability, safety, and response consistency
• Document prompt libraries and reusable interaction patterns

Tech Stack

• Models: GPT-class models, Claude, open-weight LLMs
• Languages: Python
• Frameworks: LangChain, LlamaIndex, or equivalent orchestration tools
• Evaluation: Prompt testing frameworks, vector search, RAG pipelines
• Infrastructure: API-driven architectures, cloud-native environments

Ideal Background

• Strong technical foundation, often from software engineering, ML, or computational linguistics
• Experience building production AI applications, not prototypes
• Deep understanding of LLM behaviour and token economics
• Structured thinker capable of turning language into deterministic 
• Comfortable operating in fast-moving product teams

Why Join

• Work at the forefront of applied AI
• Direct impact on products used at global scale
• Highly technical peer group
• Equity-heavy compensation aligned to company growth
• Office-first culture surrounded by top-tier engineers

Austin, Texas  

Salary: $165,000 - $200,000 + bonus + equity   

Overview

We are supporting a product-focused software company embedding machine learning directly into customer-facing applications. This role is suited to an engineer who prefers shipping models over discussing them, owning the lifecycle from training through production deployment. You will operate inside a mature engineering environment where ML is integrated into core architecture rather than isolated in research teams.

Key Responsibilities

• Design, train, and deploy machine learning models into production systems
• Build scalable feature pipelines supporting real-time inference
• Collaborate with data engineers to optimise training datasets
• Improve model performance through experimentation and monitoring
• Productionise models using containerised infrastructure
• Partner with product teams to align ML capabilities with user outcomes

Tech Stack

• Languages: Python
• Frameworks: PyTorch, TensorFlow, or JAX
• MLOps: MLflow, Kubeflow, SageMaker, or Vertex AI
• Data: Spark, Snowflake, or BigQuery
• Infrastructure: Docker, Kubernetes
• Cloud: AWS or GCP

Ideal Background

• 4+ years deploying ML models into production
• Strong software engineering fundamentals
• Experience with model monitoring and lifecycle management
• Comfortable balancing experimentation with delivery speed
• Proven ability to work within cross-functional product teams

Why Join

• Build ML systems that reach production quickly with zero bureaucracy
• Product-led environment with real user impact
• Strong funding and long-term technical vision
• High ownership with minimal hierarchy
• Competitive salary with equity upside