Data Platform Engineer - Vaga Afirmativa para Mulheres

Role Summary

Working model

📍 100% remote work model

Position challenges

Its mission will be to be the technical reference in data engineering within GCP, responsible for the backbone that supports all of the company's analytical consumption. You will architect and implement ingestion and processing pipelines — batch and near real time — on top of BigQuery, defining the orchestration, versioning and infrastructure standards as code that the rest of the team follows. It's not just about delivering pipelines: it's about designing the foundations, data contracts, and internal tools that make each new source and each new use case cheaper to build than the last.

The central challenge is to increase the maturity of our data platform and prepare the ground for our AI strategy, ensuring reliability, observability and cost predictability at scale. You'll build the foundation layer upon which analytics engineers, data scientists, and AI agents operate autonomously — with traceable lineage, monitored freshness, and governed access. In the end, whether it is a dashboard, a model or an autonomous agent consuming the data, the source of information will be unique, performant and reliable.

Responsibilities

Your day to day life at Conta Simples

• Architect and maintain the data platform on GCP, defining the ingestion, processing and orchestration standards that support the company's entire analytical consumption — from dashboards to AI agents.
• Build and evolve batch and near real time ingestion pipelines from transactional databases, APIs and events, ensuring that new sources enter BigQuery in a standardized way and with decreasing effort.
• Establish data contracts and schema governance, protecting the consumption layers from upstream breakdowns and making the agreement between those who produce and those who consume data explicit.
• Implement end-to-end data observability — lineage, freshness, volume and alerts — so that failures are detected by the team before they reach the business.
• Treating infrastructure as a product, versioning resources in Terraform, maintaining CI/CD pipelines and reducing the path between an idea and data available in production.
• Optimize performance and cost in BigQuery at scale, defining partitioning, clustering, reservations and consumption monitoring strategies so that the company's growth does not turn into proportional cost growth.
• Ensure data security and privacy by implementing access controls, masking and retention policies compatible with a regulated financial environment.
• Act as a technical reference and mentor for the team, driving architectural decisions, code reviews and the dissemination of good engineering practices applied to data.
• Collaborate directly with Analytics Engineering and Data Science, offering the basis and tools for these people to work autonomously, without depending on you for each new delivery.
• Technical seniority to make architectural decisions with explicit trade-offs and influence teams beyond your own.
• Proficiency in SQL: ability to write and debug complex, performant and readable queries.
• Solid experience in Python applied to data: building pipelines, integrations with APIs and internal tools — with tests and production quality code.
• GCP ecosystem: practical experience with BigQuery, Cloud Storage, Pub/Sub and processing services (Dataflow, Dataproc or equivalent).
• Orchestration: experience with Airflow/Cloud Composer, including idempotence, backfills and dependency management.
• Infrastructure as code and engineering culture: Terraform, Git/GitHub, CI/CD and Code Review as everyday practice.
• Data modeling: understanding of Data Warehousing and dimensional modeling enough to design layers that serve consumers well.
• Experience with Dataform or dbt and the lifecycle of versioned transformations.
• Reliability and quality: practice in automated data testing, monitoring and incident response.
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About the Company

The Data Team is the strategic foundation that transforms raw data into competitive intelligence. We operate on the border between technology and business, ensuring that information is reliable, accessible and performant and, now, the data foundation for our next frontier: generative artificial intelligence applied to business. Our challenge is to build a robust data layer that serves as fuel for the Analytics and Data Science teams to extract maximum value from our platform, supporting the company's accelerated growth with governance and efficiency.