Summary:Design and own data platforms and AI-enabled pipelines that turn raw, messy, real-world data into trustworthy products. You work end to end — from the instruments that capture data, through declarative pipelines and quality gates, to the RAG and agentic-AI workloads that sit on top — holding the line on data quality and governance so that everything built on this data can be trusted.This is a hands-on architect role: you make high-level decisions, define reference patterns, and still write code.Our operating belief — AI moves the data. Quality earns the trust.

The hardest part of AI at enterprise scale is not the model — it is the data discipline underneath it.

Responsibilities:Architect scalable, secure, and observable data platforms across Lakehouse (Bronze to Silver to Gold) and serving layers aligned to business goalsDesign and integrate data collection instruments and enforce validation at the point of data captureBuild declarative, production-grade pipelines using Databricks Lakeflow Declarative Pipelines and orchestrate them reliablyStand up and enforce auditable data-quality gates that control promotions and releasesDesign and deliver AI and GenAI workloads, including RAG systems and agentic pipelines with human-in-the-loop controlsDefine and enforce data and AI governance including lineage, access control, and responsible AI guardrailsMake high-level architectural decisions, lead design reviews and proofs of concept, and define reusable best practicesOwn staffing, interviewing, mentoring, and teaching through pairing, reviews, and documentationDesign and manage data ingestion across multiple capture mechanisms including APIs, telemetry, CDC, surveys, and regulated systemsImplement validation at source including constraints, logic, vocabularies, and consistency checksDefine data dictionaries, schemas, metadata,



and conformed models aligned with industry standardsOwn end-to-end data quality as an engineering discipline including incident detection, root cause analysis, and durable fixesBuild and maintain code-first validation frameworks embedded in CI/CD and pipelinesImplement ML-driven observability for data health across freshness, volume, schema, and distributionEnsure data quality supports trustworthy analytics and AI workloads

Required Qualifications:8 to 12+ years of experience in data engineering with progression into architecture rolesStrong Python expertise including vectorized data processing, profiling, and clean engineering practicesAdvanced SQL and solid NoSQL knowledge including optimization, indexing, and data modelingExperience with Databricks, Lakeflow Declarative Pipelines, Spark or PySpark, Delta Lake, and lakehouse architecturesExperience with at least one major cloud platform (AWS or Azure)Experience with orchestration tools such as Apache Airflow, Dagster, or PrefectProven hands-on experience with data quality engineering using code-first approaches such as dbt tests, Elementary, Great Expectations, Soda, or DeequExperience designing and integrating data collection instruments with validation at the sourceExperience designing RAG systems and working with at least one vector database such as Pinecone, Weaviate, FAISS, or MilvusStrong understanding of distributed systems concepts including CAP theorem, ACID vs BASE,



and batch vs stream processingExperience implementing CI/CD practices for data pipelinesKnowledge of data and AI governance including lineage and access control tools such as Unity Catalog, Snowflake Horizon, or Microsoft PurviewProven experience hiring, mentoring, and developing engineering talent

Preferred Qualifications:Experience with Snowflake and CortexExperience with LangChain, LangGraph, MCP, or agentic pipeline patternsFamiliarity with AI governance frameworks such as EU AI Act, NIST AI RMF, or ISO/IEC 42001 and LLM guardrailsExperience with performance and load testing tools such as k6, Locust, or JMeterUnderstanding of linear algebra applied to embeddings and similarity searchExperience with clinical or regulated data standards such as CDISC or CDASH and EDC systemsStrong BI experience including Power BI, DAX, row-level security, and performance optimizationRelevant certifications including AWS Certified Solutions Architect, Databricks, or Google Professional Data EngineerBilingual English and Spanish

Soft Skills:Strong architectural thinking with a focus on trade-offs rather than perfect solutionsComfort working under uncertainty and adapting to changing requirements and systemsCritical thinking and ability to challenge assumptions and validate conclusionsStrong problem framing skills to define constraints and success criteria before executingStrong communication and teaching mindset with the ability to mentor and grow teamsOwnership mindset with a focus on quality, governance, and long-term reliability

Must Have Skill:System-level data thinking — the ability to design, govern, and ensure quality across the entire data lifecycle, from data capture to AI consumption in production systems

#AditiConsulting#26-03825

📌 AI Data Engineer (Bogotá)
🏢 Aditi Consulting
📍 Bogotá