Staff Data Engineer
Staff Data Engineer
We are looking for a Staff Data Engineer to be the technical backbone of our Data organization. You will be part of the team owning the architecture of our data platform and making sure our data is ready for the AI era. This is a hands-on, individual-contributor role: you will spend most of your time designing and building, not managing people.
You will:
Own the technical vision and architecture of Clara's data platform, from ingestion and storage to modeling, serving and consumption
Design and build scalable, reliable, cost-efficient data pipelines (batch and streaming) that support analytics, machine learning and operational use cases
Lead our AI enablement efforts: structure, document and expose data (semantic layers, curated datasets, metadata, feature stores, retrieval-ready data) so that people, models and AI agents across the company can use it safely and effectively
Define and drive best practices in data modeling, testing, observability, CI/CD, lineage and data quality
Partner with the Director of Data to translate business and strategic goals into technical roadmaps, and to evaluate build-vs-buy decisions
Implement the technical foundations of our data governance strategy: access control, PII handling, auditability and compliance, which matter in a regulated fintech environment
Advance our self-service data platform so analysts, data scientists and business teams can work autonomously
Collaborate closely with Engineering, Product, Risk, Growth, Marketing and Operations to ensure data is captured correctly at the source
Raise the technical bar of the team through code reviews, design reviews, mentoring and technical documentation, without formal people-management responsibilities
Stretch and adapt to a dynamic and fast-paced work environment
Requirements
10+ years of experience in data engineering or software engineering with a data focus, including significant experience building and scaling data platforms at a top-tier tech company (fintech/startup experience is a plus)
Proven track record designing and operating production-grade data architectures (lakehouse/warehouse, ELT/ETL, orchestration, streaming) at scale
Deep proficiency in SQL and Python; experience with distributed processing frameworks (e.g. Spark) and orchestration tools (e.g. Airflow, Dagster)
Experience building tooling for AI agents or LLM applications on top of enterprise data (text-to-SQL, MCP, RAG pipelines)
Hands-on experience preparing data for ML and AI use cases (feature engineering, vector stores/embeddings, semantic layers, LLM-ready data) or a clear track record of moving quickly into this space
Strong experience with cloud data platforms and infrastructure (AWS, GCP or Azure), plus infrastructure-as-code and CI/CD practices
Strong command of data modeling, data quality, observability and governance best practices
Experience with modern data stack tools (e.g. dbt, Databricks, Snowflake, BigQuery, Kafka)
Ability to make sound architectural trade-offs (scalability, cost, reliability, speed of delivery) and communicate them clearly to technical and non-technical audiences
Strong interpersonal and communication skills; comfortable influencing without authority and working closely with a Director-level leader
Bachelor's degree in Computer Science, Engineering, Math, or another relevant quantitative discipline (or equivalent practical experience)
Nice to have:
Experience in payments, credit, cards or other regulated financial services
Experience with data catalogs, metadata management and data contracts
Open-source contributions or technical writing/speaking
Where we work
This is a hybrid role: you'll split your time between the office, customers and home. We don't set a minimum number of office days for most roles — you'll spend time in the office naturally, and most days during your ramp-up or when your leader asks.