Responsibilities
- Design and develop scalable data systems for analytics, ML/AI products, reporting, APIs, integrations, and customer-facing data products.
- Manage the entire data lifecycle including ingestion, transformation, modeling, validation, lineage, publishing, and serving across creative, media, customer, model-output, and performance data.
- Create scalable, observable, replayable, and cost-efficient batch and near-real-time pipelines.
- Collaborate with Data Science to operationalize models, scores, embeddings, prompts, evaluations, and experimental outputs into reliable production data products and customer-facing capabilities.
- Develop data foundations for AI-powered products using LLMs, VLMs, multimodal analysis, agent workflows, and reinforcement learning.
- Establish best practices for data contracts, pipeline design, testing, reviews, observability, and production readiness.
- Build governed datasets and serving patterns for dashboards, APIs, exports, partner integrations, ML workflows, benchmarks, and agent-ready use cases.
- Construct reliable data flows with ad platforms, DSPs, measurement partners, creative systems, customer environments, and internal product surfaces.
- Influence long-term decisions on tooling, storage, processing frameworks, serving patterns, governance, and cost structure.
Requirements
- 8+ years in data engineering, data platform engineering, or analytics engineering in SaaS, platform, AdTech, MarTech, marketplace, or other data-intensive environments.
- Deep expertise with modern warehouse, lakehouse, orchestration, and transformation technologies such as Snowflake, Databricks, BigQuery, relational and noSQL databases.
- Production-grade SQL and strong programming skills in Python, Scala, Java, or similar languages.
- Experience designing batch, incremental, and near-real-time processing systems at scale.
- Experience designing high-volume processing pipelines and their surrounding architecture.
- Experience building or supporting production products powered by ML or AI, including LLMs, VLMs, embeddings, recommendation systems, or multimodal data products.
- Strong ability to productionize models, monitor outputs, build feedback loops, and make experimental work reliable at product scale.
- Experience implementing validation, monitoring, lineage, alerting, incident response, and data trust practices for critical pipelines and model-output workflows.
- Active use of AI tools to accelerate development, testing, documentation, data discovery, root-cause analysis, and operational workflows.
- Pragmatic technical leadership in building reusable platform patterns, shipping tactical fixes, and preventing one-off work from becoming permanent architecture.
- Excellent writing and artifact discipline with ability to explain architecture, data quality risks, and tradeoffs clearly to technical and non-technical audiences.
- B2+ written and spoken English fluency.
- Ownership of outcomes, not just pipelines.
Nice to Have
- Typescript knowledge is a plus.
- Familiarity with AdTech, MarTech, platform APIs, ML-powered products, or customer-facing analytics products is a major plus.
Work Arrangement
Mostly remote with periodic international travel within Latam or to the US required.
Other
- Strong written and spoken English skills are essential as the global team works in English.
- This position is mostly remote, but periodic international travel within Latam or to the US will be required.
- B2+ written and spoken English fluency is required.
