Senior Data Engineer

About Indicium AI

Indicium AI is trusted by the world's leading enterprises to deliver AI into production at scale. We are a global, AI-native consultancy with deep expertise across Financial Services, Energy & Utilities, Healthcare & Life Sciences, Retail & CPG, and Manufacturing - guiding organizations from strategy through build to measurable business outcomes. With 600+ AI experts, 50+ enterprise clients, and five global locations, we work side-by-side with the world's leading AI partners - including Anthropic, Databricks, AWS, OpenAI, and Microsoft - to deliver modern AI with speed, clarity, and lasting impact.

As a global company, we offer career opportunities across Europe and the Americas and are looking for talent eager to grow globally with our team. Please indicate in your application if you are available to work abroad and if you hold a European passport.

About the Opportunity

The Senior Data Engineer will act as the technical reference for data platforms on projects for industrial clients, modernizing lakehouses on Azure Databricks so they can support domain-based data products and AI use cases in operations. The role spans from diagnosing legacy platforms to designing the architecture for ingestion, environments, security, and cost control in production.

The position involves redesigning ingestion patterns with history preservation, separating development and production environments, protecting sensitive data in compliance with the LGPD (Brazilian General Data Protection Law), and reducing costs measurably. It also includes leading platform decisions within the squad, documenting them, and mentoring mid-level and junior data engineers, as well as preparing the handover to the support team.

We are looking for a hands-on professional who writes code, defends architecture decisions with data, and is driven to see stable, secure, and efficient platforms running in real-world operations.

Key Responsibilities

• Redesigning ingestion with a raw/landing layer that preserves history, replacing full loads with overwrite by incremental and CDC patterns from SQL Server staging and source systems (ERP, HR, maintenance, SSMA, and operational systems).
• Defining the lakehouse architecture: medallion layers, separate DEV/HML/PRD environments, and a domain-based Unity Catalog strategy.
• Implementing data security and privacy by removing credentials from code (Key Vault, secret scopes, and service principals) and applying column masks, row filters, and ABAC in Unity Catalog to classified personal data, in compliance with the LGPD.
• Orchestrating pipelines with Lakeflow Jobs and Databricks Asset Bundles, using infrastructure as code (Terraform) and CI/CD on Azure DevOps, including linting, testing, and promotion across environments.
• Ensuring platform reliability through alerts with clear ownership and escalation, data refresh SLAs, data quality expectations, and observability using Databricks system tables.
• Leading platform FinOps (serverless vs. classic compute, cluster policies, budgets, and tags), with measurable cost reduction.
• Taking over and stabilizing legacy pipelines, leading incident handling, creating runbooks, and transferring knowledge to the support team.
• Supporting, when needed, the ingestion of industrial time series (process historian) for operational intelligence products.
• Reviewing code, writing ADRs, and mentoring mid-level and junior data engineers.
• Having the freedom and critical thinking to propose and challenge data engineering solutions.

Preferred Qualifications & Experience

• Senior experience in production data engineering with Python, PySpark/Spark SQL, and Delta Lake.
• Production experience with Databricks: Unity Catalog, Jobs/Lakeflow, Databricks Asset Bundles, and SQL Warehouse.
• Strong command of ingestion patterns: CDC, incremental loads, Auto Loader, JDBC, REST APIs, and schema evolution.
• Experience with infrastructure as code (Terraform) and CI/CD for data platforms.
• Hands-on experience with data security and privacy: personal data masking, access models, and LGPD.
• Track record of reducing costs on cloud data platforms.
• Experience taking over and refactoring legacy data platforms.
• Ability to communicate technical and cost trade-offs to technical leadership and to defend architecture decisions against third-party recommendations.
• Autonomy to deliver at a fast pace in legacy environments, without a lon