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Staff Data Engineer who sets the architecture and technical direction of Coalition's enterprise Data & Analytics platform, building scalable Snowflake/dbt pipelines, governed data products, and AI/ML foundations (feature stores, vector search, RAG) that power cyber insurance, actuarial, and BI workflows.
Builds ETL pipelines, data models, and reusable analysis functions for LILA's scientific data platform, turning raw lab instrument outputs into validated, analysis-ready datasets across biology, chemistry, and materials science. Core stack: Python, SQL/Postgres, and workflow orchestration (Flyte, Airflow, Prefect, Dagster, or Nextflow).
Senior backend software engineer on LILA Sciences' Data Platform Team, building the data backbone of its autonomous 'AI Science Factory' — high-performance APIs, SQL/NoSQL/vector database systems, workflow orchestration, and cloud infrastructure (AWS, Kubernetes) that turn lab and instrument data into faster scientific discovery.
Senior software engineer on LILA Sciences' Application team, building the full-stack, AI-native platform that lets scientists collaborate with AI agents — designing UI and APIs, scaling SQL/NoSQL/vector databases, and running workloads on AWS and Kubernetes to connect AI to automated lab workflows.
Senior full-stack engineer on LILA Sciences' Scientific System of Record team, building the systems that capture experiment plans, lab execution, and results. Day-to-day spans React/TypeScript UIs, Python backend services and APIs, data modeling across SQL/NoSQL/vector stores, and deployment on AWS and Kubernetes.
Designs and builds the AI-native application layer of LILA's scientific discovery platform: full-stack UIs and APIs (React, TypeScript, FastAPI, Python), database architecture across SQL/NoSQL/vector stores, and AWS/Kubernetes infrastructure so researchers can collaborate with AI agents from hypothesis to experiment.
Lead architecture and development of an AI-driven scientific platform that integrates lab workflows, data capture, and analytics, using TypeScript, React, Python, and modern cloud infrastructure.
Hands-on senior engineer role designing and scaling Orca AI's data platform: building high-volume batch and streaming ingestion pipelines, evolving the data lake, and managing Kubernetes infrastructure with Terraform and KEDA to power analytics and AI workloads. Core stack includes Python, SQL, Snowflake, and orchestration tools like Airflow or Prefect.
We're building a connected, end-to-end Enterprise AI engine - uniting data foundations, AI technology, process reinvention, and business-facing AI to accelerate results across the whole value chain. Success depends…
Job Title: Senior Python Developer Reports to : Lead Machine Learning Engineer Intelligent Audit is a rapidly expanding freight audit and business analytics technology company helping our customers become smarter…
We have an opening for a Data Engineer to build and modernize data systems that make mission-critical scientific and engineering data discoverable, reusable, and available for analysis. You will partner with…
Data Engineer at Generali Personenversicherung in Adliswil: design, build and run scalable ETL/ELT pipelines and cloud data platforms (AWS, Python/SQL, PySpark, Airflow, Redshift/Snowflake/Databricks) to make complex insurance data reliable and usable for analytics, pricing and machine learning. Hybrid role with up to 60% remote work.
Lead and line-manage a small data science team at myPOS, a European payments company, while staying hands-on: building and shipping production ML models for churn, customer lifecycle, cross-sell, and fraud/risk on a GCP stack (BigQuery, Vertex AI MLOps), with some LLM/agentic workflow work.
Senior engineer owning the data and AI layers of a client's AI-powered analytics platform: building CRM ingestion pipelines, PostgreSQL data models and metrics layers, LLM tool-calling interfaces, and RAG with hybrid search. Core stack: Python, PostgreSQL, Airflow/dbt, LLM APIs, pgvector, GCP/AWS.
Senior Software Engineer on Flatiron Health's Healthcare Data Products team in London, building scalable pipelines and Snowflake-based platforms that turn clinical cancer data into real-world evidence. Day to day: data engineering with Python/PySpark/Airflow, Snowflake-native data sharing, and full-stack work across Python backends and React frontends.
Lead a team building and scaling the data ingestion layer that feeds AI training corpora, including web crawling, extraction pipelines, and data lakes, while staying hands-on with distributed systems and collaborating closely with research teams.
Lead a data engineering team building scalable pipelines and ETL processes in Python/SQL to power AI-driven transportation and infrastructure analytics for U.S. clients.
Data engineering at J4RVIS means building the foundations that make enterprise data reliable, accessible, and ready to drive business decisions. You’ll design and own production data pipelines, transform raw data into…
We are a healthcare technology company that provides platforms and solutions to improve the management and access of cost-effective pharmacy benefits. Our technology helps enterprise and partnership clients simplify…
Hands-on leadership role in Netcompany's Data Intelligence & AI practice: lead a small data engineering team delivering cloud-native data lake/lakehouse platforms (medallion architecture) on Azure, AWS or Databricks for client engagements. Core stack: SQL, Python, Spark, ELT pipelines, Airflow/Dagster, Delta Lake/Iceberg, CI/CD and DataOps.
