Mira encuentra roles remotos de TI a los que realmente puedes aplicar.

Own the analytical strategy for a product area: identifying the highest-leverage questions, defining the measurement framework, and ensuring data-driven decisions

Build the pipes: You’ll build and manage our business data pipelines as well as transform Firefox telemetry data into structured high quality datasets Mozillians can rely on

Build the pipes: You’ll build and manage our business data pipelines as well as transform Firefox telemetry data into structured high quality datasets Mozillians can rely on

Build the pipes: You’ll build and manage our business data pipelines as well as transform Firefox telemetry data into structured high quality datasets Mozillians can rely on

Own end-to-end development of multi-agent AI systems, from architecture and implementation through testing, deployment, and ongoing operation

Own end-to-end development of multi-agent AI systems, from architecture and implementation through testing, deployment, and ongoing operation

Own end-to-end development of multi-agent AI systems, from architecture and implementation through testing, deployment, and ongoing operation

Own end-to-end development of multi-agent AI systems, from architecture and implementation through testing, deployment, and ongoing operation

Design, build, and operate core components of our lakehouse platform, including Apache Iceberg table management (data compaction, data layout optimization, materialized view scheduling…) and Iceberg catalog

Act as a subject matter expert for data observability on behalf of the Datadog advocacy and engineering teams

Over 6 years of experience as a Data Engineer

Work across the entire Linux stack, from kernel, networking, storage, to applications,

Design and manage scalable data platforms powering real-time analytics, batch processing, and exploratory analysis, using AI-assisted development as the default workflow, not an afterthought.

Motivated by designing scalable data platforms that serve hundreds of stakeholders.

Design, build, and manage reliable data ingestion and transformation pipelines using Airflow, dbt, Python, Matillion, and Snowflake.

Create data pipelines to ingest data from Workday and the conservation Hub into a shared data lake for future analysis.

Help define company data assets(data model), Spark, SparkSQL jobs to populate data models

Assemble large, complex data sets that meet business requirements through extraction, transformation, and loading of data from a wide variety of data sources.

Own AI powered features from design through deployment, partnering with product, design, and engineering to scope work and define success metrics.

Production Lifecycle: Lead the design and implementation of end-to-end ML lifecycles, focusing on automated CI/CD pipelines, model versioning (MLflow/DVC), and reproducible experimentation.

Design, train, and deploy computer vision models to production with well-understood performance, latency, and cost characteristics.

Design and evolve a scalable, secure, cloud‑native lakehouse platform leveraging Databricks, Microsoft Fabric (OneLake, Lakehouse, Data Factory), and dbt.

Design and maintain event-driven pipelines from backend systems (Ruby on Rails) to Snowflake.