9 days ago
Mexico City, MexicoStaff+
Responsibilities
- Own the technical vision and architecture of Clara’s data platform from ingestion and storage through modeling, serving, and consumption.
- Design and build scalable, reliable, cost-efficient batch and streaming data pipelines.
- Structure, document, and expose curated, governed, semantic, metadata-rich, feature-store, and retrieval-ready data for people, models, and AI agents.
- Define and drive best practices for data modeling, testing, observability, CI/CD, lineage, and data quality.
- Partner with the Director of Data on technical roadmaps and build-versus-buy decisions.
- Implement data governance foundations including access control, PII handling, auditability, and compliance.
- Advance the self-service data platform for analysts, data scientists, and business teams.
- Collaborate with Engineering, Product, Risk, Growth, Marketing, and Operations to ensure correct source data capture.
- Raise the team’s technical bar through code reviews, design reviews, mentoring, and technical documentation without formal people-management responsibilities.
Requirements
- 10+ years of data engineering or software engineering experience with a data focus, including significant experience scaling data platforms at a top-tier technology company.
- Proven experience designing and operating production-grade data architectures involving lakehouses or warehouses, ELT/ETL, orchestration, and streaming at scale.
- Deep proficiency in SQL and Python, with experience using distributed processing frameworks and orchestration tools.
- Experience building tooling for AI agents or LLM applications on enterprise data, including text-to-SQL, MCP, or RAG pipelines.
- Hands-on experience preparing data for ML and AI use cases, including feature engineering, vector stores or embeddings, semantic layers, or LLM-ready data.
- Strong experience with AWS, GCP, or Azure, 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 such as dbt, Databricks, Snowflake, BigQuery, and Kafka.
- Ability to make architectural trade-offs and communicate decisions to technical and non-technical audiences.
- Strong interpersonal and communication skills, including the ability to influence without authority and work with a Director-level leader.
- Bachelor’s degree in Computer Science, Engineering, Math, or another relevant quantitative discipline, or equivalent practical experience.
- Preferred experience includes payments, credit, cards, regulated financial services, data catalogs, metadata management, data contracts, open-source contributions, or technical writing and speaking.
Benefits
- Hybrid work split between the office, customers, and home, with no minimum office-day requirement for most roles.
- Competitive salary and stock options through an ESOP from day one.
- Annual learning budget and accelerated development paths.
- English is the working language, with Spanish and Portuguese used daily.
- High-ownership, low-ego team environment focused on impact in Latin America.
Tech Stack
Apache AirflowApache KafkaApache SparkAWSAzureDatabricksdbtGoogle BigQueryGoogle Cloud PlatformPythonSnowflakeSQL
Categories
Data Engineering
About Clara
CLARA Analytics builds AI-driven software to help property and casualty insurers and self-insured organizations manage claims, reduce litigation risk, and improve provider selection and outcomes. Its products are sold on a subscription basis and integrate with existing claims systems to deliver triage, severity prediction, and workflow insights. The company was founded in 2016 and is headquartered in Santa Clara, California.