Senior Data Platform Architect for AI-Driven Pipelines

Aditi Consulting

Bogotá ciudad

Presencial

COP 110.000.000 - 160.000.000

Jornada completa

hace 29 horas

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Descripción de la vacante

Aditi Consulting in Bogotá seeks a senior data architect to design and own data platforms and AI-enabled pipelines that convert raw real-world data into trustworthy products. You will own end-to-end data quality, governance, and RAG/agentic AI workloads with human‑in‑the‑loop controls.

You’ll write code, define reference patterns, and lead architectural reviews. Responsibilities include building scalable data platforms, enforcing validation, and orchestrating production pipelines while mentoring

Formación

8–12+ years in data engineering with progression into architecture roles.
Strong Python expertise with vectorized data processing and clean engineering practices.
Advanced SQL and NoSQL knowledge with optimization, indexing, and data modeling.
Experience with Databricks, Lakeflow Declarative Pipelines, Spark or PySpark, Delta Lake, and lakehouse architectures.
Experience with at least one major cloud platform (AWS or Azure).
Experience with orchestration tools such as Airflow, Dagster, or Prefect.
Hands-on data quality engineering using code-first approaches such as dbt tests, Great Expectations, Soda, or Deequ.
Experience designing and integrating data collection instruments with validation at the source.
Experience designing RAG systems and vector databases like Pinecone, Weaviate, FAISS, Milvus.
Strong understanding of distributed systems concepts (CAP, ACID vs BASE, batch vs stream).
Experience implementing CI/CD for data pipelines.
Knowledge of data and AI governance tools like Unity Catalog, Snowflake Horizon, or Microsoft Purview.
Proven experience hiring, mentoring, and developing engineers.

Responsabilidades

Architect scalable, secure, and observable data platforms end‑to‑end across Lakehouse layers.
Design and integrate data collection instruments with source validation.
Build production-grade pipelines using Lakeflow Declarative Pipelines and orchestrate them reliably.
Enforce auditable data‑quality gates for promotions and releases.
Design and deliver AI and GenAI workloads, including RAG systems with human‑in‑the‑loop controls.
Define and enforce data and AI governance including lineage and access control.
Make high‑level architectural decisions, lead design reviews and proofs of concept, and define reusable best practices.
Own staffing, interviewing, mentoring, and teaching through pairing and reviews.
Design and manage data ingestion across APIs, telemetry, CDC, surveys, and regulated systems.
Implement validation at source including constraints, logic, vocabularies, and consistency checks.
Define data dictionaries, schemas, metadata, and conformed models with industry standards.
Own end‑to‑end data quality as an engineering discipline including incident handling.
Build and maintain code‑first validation frameworks embedded in CI/CD and pipelines.
Implement ML‑driven observability for data health (freshness, volume, schema, distribution).
Ensure data quality supports trustworthy analytics and AI workloads.