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Salary
$41k – $100k per year (Estimated)
Location
Remote/Hybrid (Lisbon, Portugal)
Seniority
Senior
Employment
Full-Time
Overview
Company
Impact
Profile match
Ermeo digitises field operations by replacing paper procedures with guided mobile workflows for technicians. Its platform works offline and feeds inspection and maintenance data straight back into company systems. The company is headquartered in Paris.

Senior Data Engineer

Lisbon - Hybrid

Do you want to help shape software that affects thousands of lives?

Who are we?

We are ranked as the UK’s #1 construction specific software player and our mission is simple; to provide market leading end-to-end software solutions to the construction and construction like industries across the entire build life cycle.

If you are looking to build an exceptional career with an award-winning company you’ve come to the right place. Our teams are based in the UK, Europe, and India, working on products that are used on a global scale. We have a clear and defined road map to deliver over the next 3 years, which is centred around a large-scale digital transformation as well as continuing our growth and expansion.

We embrace diversity and equality and want our employees to be comfortable bringing their whole selves to work. We are committed to building a team with a variety of backgrounds, skills and views. Creating a culture of Equality isn’t just the right thing to do, it improves every aspect of our business.

Purpose

The Senior Data Engineer plays a central role in building Causeway’s next-generation data platform on Databricks, a platform that unifies data from across the Causeway product estate and a wide range of third-party sources into a typed, entity-resolved digital twin that AI agents can reason over.

The Senior Data Engineer owns significant slices of the medallion pipeline (bronze to silver to gold), the modelling layer that turns raw Delta tables into typed object and relationship tables, and the serving layer that makes the graph queryable by agents with interactive latency. The role is expected to design for scale, set the bar for engineering quality across the team, and make architectural decisions that are durable.

The key areas of responsibility for the Senior Data Engineer include:

  • Medallion Pipeline & Graph Engineering,
  • Data Quality & Governance,
  • Technical Leadership & Collaboration.

Key Responsibilities

Medallion Pipeline & Graph Engineering

  • Design and own ingestion into the bronze layer from a variety of sources, selecting the appropriate pattern per source. Including Auto Loader, API pull, event-driven, database replica/CDC, and CQRS read models and agreeing data contracts with Architects and Product teams.
  • Build and maintain the silver and gold medallion layers as Delta Live Tables and PySpark notebooks, handling deduplication, entity resolution (deterministic and probabilistic), canonical ID assignment, and projection into typed object and relationship tables.
  • Generate and maintain the typed relationship graph that transforms disconnected gold tables into a traversable digital twin.
  • Own and evolve the serving layer that makes the graph queryable by AI agents at interactive latency.
  • Contribute to the design and evolution of the broader Causeway data platform architecture, ensuring solutions are scalable, maintainable, and aligned with cloud-native best practices.

Data Quality & Governance

  • Own the data quality and testing story end-to-end, implementing automated frameworks that validate completeness, accuracy, consistency, and schema conformance across pipeline stages.
  • Deliver governance via Unity Catalog, covering multi-tenant isolation, row-level security, lineage, audit logging, and the tenant-scoped extension schema that supports custom objects from external customer systems.
  • Ensure data assets are catalogued correctly with appropriate metadata, security classifications, and sensitivity labels in line with company data governance standards.
  • Monitor pipeline health and data quality metrics, proactively identifying and resolving issues before they affect downstream consumers or agent behaviour.

Technical Leadership & Collaboration

  • Mentor engineers across the data team, lead architectural discussions, conduct PR reviews, and challenge poor patterns, including those already in production.
  • Partner with the Platform and Agent teams on the tool layer that exposes the graph to AI agents, ensuring datasets are agent-legible through rich column comments, typed relationships, embedding templates, and governed tooling.
  • Work with Data Engineers to translate complex analytical requirements into structured, reusable datasets and features.
  • Contribute to the continuous improvement of data engineering practices, tooling, and standards across the team.
  • Take responsibility for personal development, staying current with emerging technologies in data engineering, lakehouse architecture, and AI agent tooling.

Key Skills, Experience and Qualifications

Essential

  • Strong production experience on Databricks, including Delta Lake, Delta Live Tables, Auto Loader, Unity Catalog, Databricks Asset Bundles, serverless and job compute, and Structured Streaming for near-real-time workloads.
  • Deep proficiency in Python and PySpark for large-scale data processing and transformation.
  • Advanced SQL, including recursive CTEs for graph traversal, window functions, query planning, and the ability to interpret EXPLAIN output and optimise index usage.
  • Strong experience with lakehouse architectures and data modelling for graph workloads - typed entities, edge tables, dimensional vs event-style modelling, and entity resolution across systems that do not share keys.
  • Hands-on experience with PostgreSQL as a serving layer, including pgvector for semantic search, pg_trgm for fuzzy matching, HNSW vs IVFFlat trade-offs, index tuning, and managing interactive-latency queries under concurrency.
  • Expertise in data transformation, validation, and contract design.
  • Experience building data test and observability frameworks.
  • Working knowledge of cloud storage and identity across AWS, Azure, and GCP - including S3, ADLS Gen2, GCS, cross-cloud copy, IAM roles, and storage credentials.
  • Genuine curiosity about AI agents and how they consume data, with an understanding of what makes a dataset agent-legible.

Desirable

  • Experience with probabilistic entity resolution techniques and tools.
  • Familiarity with knowledge graph concepts and graph query languages.
  • Exposure to AI/ML workflows and feature engineering for machine learning models.

What you get from us:

If you're looking to build an exceptional career with an award-winning company you’ve come to the right place. We believe everyone at Causeway has a vital role to play in our success. Causeway is fuelled by curiosity and is a place for people who beam with positivity and burn with ambition.

Our team is everything, so we’ll take good care of you. In fact, we give well-being the same priority as our other business goals. We’re strong advocates of work-life balance, offering hybrid working alongside the opportunity to work from modern, collaborative offices.

Our Values

We areUnited. As part of a team, we’re better together.

We areAgile. Be the change, we’re on a journey.

We areTrusted. Do the right thing, we own this.

We are Driven. Get stuck in, we make it happen.

Like all responsible companies Causeway is aware of the need to recognise the importance of protecting our environment and addressing the climate emergency. Causeway is a carbon neutral company and we offset our calculated carbon footprint. However, we recognise that offsetting is not a permanent solution, so we set environmental objectives to reduce our footprint year-on-year.

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