Senior Data Engineer

Porto,PortoRemoteFull Timesenior

Data EngineeringData EngineerDataData & AI

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Overview

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At Sword, we’re building AI to heal billions and unlock humanity’s full potential. In doing so, we’re pioneering AI Care, a fundamentally new approach to healthcare built for medical reasoning, safety, and real-time treatment, not generic technology applied after the fact. As both a clinical-centric frontier AI lab and an applied AI platform, Sword is reimagining how care is delivered at scale, removing traditional barriers like appointments, waiting rooms, and stigma so more people can access the care they need—and ultimately get back to lives lived in full.

Since 2020, Sword has expanded across physical therapy, women’s health, cardiometabolic, and mental health, and is now moving beyond the session to a fully AI-native, 24/7 care program that brings physical activity, therapeutic exercise, psychotherapy, nutrition, and behavior change into one connected experience. More than 700,000 members across three continents have completed over 10 million AI sessions, helping 1,000+ enterprise clients avoid more than $1 billion in unnecessary healthcare costs. Backed by 42 clinical studies, 44+ patents, and more than $500 million raised from leading investors including Khosla Ventures, General Catalyst, and Founders Fund, Sword is defining a new standard for healthcare.

At Sword, data is core to our mission to build a pain-free world. The Data Core team owns the batch and analytics layer that powers company-wide reporting, clinical insight, commercial analytics, and decisions across the business - the pipelines, models, and warehouse that every stakeholder at Sword depends on.

This role is for an engineer who loves the craft of turning raw data into trusted, modeled, analysis-ready datasets. You’ll design and operate the batch pipelines that land clean data in the warehouse, own the transformation layer, and be the partner that analysts, product managers, and clinical researchers rely on when they need to make a decision.

AI Proficiency at Sword Health

AI fluency is a core expectation at Sword Health. Every candidate is assessed against our three-level framework — be ready to share real examples of how AI is already part of how you work.

Explorer (Level 1) — Uses AI daily to boost personal productivity

Builder (Level 2) — Creates workflows and tools that elevate the whole team

Integrator (Level 3) — Embeds AI into products and processes at scale

Every hire must demonstrate at least Level 1. The expected level will vary depending on the seniority of the role.

Design and operate the batch pipelines that power Sword’s warehouse and reporting — analytics, product, commercial, and clinical teams depend on what you ship.

Model data for analytics at scale: dimensional modeling, semantic layers, metric definitions that hold up across many stakeholders.

Own the transformation layer and make it something downstream users enjoy building on.

Build the curated, trusted datasets the rest of the business consumes — from source systems all the way through to the warehouse serving layer.

Support analysts, PMs, and clinical researchers in adopting warehouse data by raising the level of abstraction, not by writing their queries.

Contribute to lineage, governance, and documentation so what you model is discoverable and trusted.

Build and maintain AI-ready data infrastructure that feeds ML and AI products.

Leverage AI coding assistants and LLMs to accelerate development, automate documentation, and raise pipeline quality.

Strong experience building batch pipelines and analytics datasets at scale.

Proficiency with Python and SQL - SQL especially; this role leans analytics.

Deep data modeling skills: dimensional, Data Vault, or similar; semantic layer design; metric definitions.

dbt in production as a first-class skill - you know how to structure a large dbt project, design for testability, and keep it maintainable as it grows.

Hands-on experience with at least one modern warehouse or lakehouse engine - Snowflake, BigQuery, Databricks, or Trino/Starburst.

Production experience with a workflow orchestrator (Airflow, Dagster, or similar).

Clear communicator: you can talk to PMs, analysts, and clinicians without drowning them in jargon.

Pragmatic: you ship the 80% solution and iterate.

Ownership: you don’t hand off broken pipelines.

Bonus:

Familiarity with lakehouse table formats (Iceberg, Delta, Hudi).

Understanding of Kafka and event-driven sources, enough to consume them into batch layers sensibly.

Streaming exposure - Flink or Spark Structured Streaming - for when batch isn’t enough.

Experience with reverse-ETL, metrics stores, or semantic layer tooling (Cube, LookML, MetricFlow).

Experience in healthcare, HIPAA, or FedRAMP environments.

PySpark or Spark SQL at scale.