Purpose Green
Senior Engineer Data, AI & Analytics (m/w/d)
Purpose Green, Welcome, South Carolina, United States
Your Skills and Qualifications
Must-Haves
Strong communication and stakeholder management in both English and German (written + spoken) Production-level experience in Python for data and AI engineering (pipelines, APIs, orchestration) Solid SQL and data modeling expertise (including incremental strategies) Hands-on experience with a major cloud provider (AWS, GCP, or Azure) Terraform/IaC experience for provisioning cloud infrastructure Experience using at least one modern BI tool (e.g., Metabase, Power BI, Looker) Nice-to-Haves
Deep AWS experience (S3, Lambda, Glue, Redshift, OpenSearch) Hands-on experience deploying AI/LLM-based systems into production Experience using dbt Cloud for transformation pipelines Familiarity with tracing and observability (e.g., Langfuse, OpenTelemetry) Experience preparing datasets and running supervised fine-tuning (SFT) of LLMs Exposure to reverse ETL tools (e.g., Census, Hightouch) or building custom syncs to HubSpot, Slack, APIs Welcome
We’re looking for a senior-level engineer to take full ownership of our data and AI systems, from ingestion and modeling to embedding pipelines and LLM-based applications. You’ll operate across domains (data infrastructure, BI, and AI), working closely with stakeholders in product, operations, and engineering. This is a full-stack data role
with a dual mandate: Build and scale
our AI-native data platform Help shape and lead
a growing Data & AI team as we scale We’re early-stage, so you’ll move between strategy and code, long-term design and fast iteration. You’ll lay the groundwork not just for the system, but for the team that will own it. Your Responsibilities
AI & Application Engineering
Architect and deploy AI-powered systems into production (e.g., FastAPI apps with RAG architecture using OpenSearch and Langfuse) Optimize agentic workflows, prompt & embedding pipelines, and retrieval quality through experimentation and tracing Extend LLM capabilities with supervised fine-tuning (SFT) for in-domain data distributions where RAG alone underperforms Data Engineering
Build scalable ingestion and transformation pipelines for both proprietary and external data (using Lambda, Glue, Terraform) Own embedding pipelines for retrieval-augmented generation systems Manage infrastructure-as-code for all core components (Redshift, S3, VPC, IAM, OpenSearch) Analytics Engineering & BI
Maintain and evolve a clean, documented data model (dbt Cloud ⇒ leverage Fusion) Develop and maintain BI dashboards in QuickSight and/or Metabase Provide ad-hoc analytical support for product, sales, and ops teams Build event-driven automation and reverse ETL pipelines to serve data or AI outputs back into operational systems (e.g., HubSpot) Leadership & Collaboration
Work closely with stakeholders across engineering, product, and operations to define the right data products and abstractions Lay the foundation for a high-performing Data & AI team. Help hire, mentor, and establish best practices as we grow
#J-18808-Ljbffr
Must-Haves
Strong communication and stakeholder management in both English and German (written + spoken) Production-level experience in Python for data and AI engineering (pipelines, APIs, orchestration) Solid SQL and data modeling expertise (including incremental strategies) Hands-on experience with a major cloud provider (AWS, GCP, or Azure) Terraform/IaC experience for provisioning cloud infrastructure Experience using at least one modern BI tool (e.g., Metabase, Power BI, Looker) Nice-to-Haves
Deep AWS experience (S3, Lambda, Glue, Redshift, OpenSearch) Hands-on experience deploying AI/LLM-based systems into production Experience using dbt Cloud for transformation pipelines Familiarity with tracing and observability (e.g., Langfuse, OpenTelemetry) Experience preparing datasets and running supervised fine-tuning (SFT) of LLMs Exposure to reverse ETL tools (e.g., Census, Hightouch) or building custom syncs to HubSpot, Slack, APIs Welcome
We’re looking for a senior-level engineer to take full ownership of our data and AI systems, from ingestion and modeling to embedding pipelines and LLM-based applications. You’ll operate across domains (data infrastructure, BI, and AI), working closely with stakeholders in product, operations, and engineering. This is a full-stack data role
with a dual mandate: Build and scale
our AI-native data platform Help shape and lead
a growing Data & AI team as we scale We’re early-stage, so you’ll move between strategy and code, long-term design and fast iteration. You’ll lay the groundwork not just for the system, but for the team that will own it. Your Responsibilities
AI & Application Engineering
Architect and deploy AI-powered systems into production (e.g., FastAPI apps with RAG architecture using OpenSearch and Langfuse) Optimize agentic workflows, prompt & embedding pipelines, and retrieval quality through experimentation and tracing Extend LLM capabilities with supervised fine-tuning (SFT) for in-domain data distributions where RAG alone underperforms Data Engineering
Build scalable ingestion and transformation pipelines for both proprietary and external data (using Lambda, Glue, Terraform) Own embedding pipelines for retrieval-augmented generation systems Manage infrastructure-as-code for all core components (Redshift, S3, VPC, IAM, OpenSearch) Analytics Engineering & BI
Maintain and evolve a clean, documented data model (dbt Cloud ⇒ leverage Fusion) Develop and maintain BI dashboards in QuickSight and/or Metabase Provide ad-hoc analytical support for product, sales, and ops teams Build event-driven automation and reverse ETL pipelines to serve data or AI outputs back into operational systems (e.g., HubSpot) Leadership & Collaboration
Work closely with stakeholders across engineering, product, and operations to define the right data products and abstractions Lay the foundation for a high-performing Data & AI team. Help hire, mentor, and establish best practices as we grow
#J-18808-Ljbffr