Data Engineer – Data Platforms, AI Tooling

Job not on LinkedIn

🕒 August 14

💃 Latin America – Remote

💵 $5.4k - $7.9k / month

⏰ Full Time

🟡 Mid-level

🟠 Senior

🚰 Data Engineer

👻 Ghost score 4%

What it means: how likely this posting is sitting open without active hiring.How to read it: 60% or higher means we think this is a ghost. Lower scores are less of a worry.How we score it: age, reposts, open roles vs team size, how fast jobs close, and whether pay is listed.Fine print: an estimate from public data, not a judgment of the company. Real jobs can score high, so treat it as a hint, not a fact.

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ITX Corp.

WebsiteLinkedInAll Job Openings

201 - 500 employees

Founded 1997

💼 Consulting

📣 Marketing

📦 Logistics

Consulting • Marketing • Logistics

ITX Corp. is a full-service custom software partner that has been a transformative player in software since 1997. They specialize in software product strategy, architecture and security, user research and design, and data science and artificial intelligence. ITX collaborates closely with clients to solve major software product challenges and employs AI and machine learning to enhance decision-making and business success. Their work spans various industries, including financial services, civic and social services, insurance services, and eCommerce, ensuring functionality, compliance, and security. ITX is committed to accessibility, demonstrating technological and organizational maturity through SOC 2 Type II compliance and adherence to web accessibility guidelines.

📋 Description

• Build and maintain scalable batch and streaming data pipelines from applications, databases, APIs, event streams, and third-party systems • Develop trusted, reusable data products for analytics, business applications, and AI solutions • Design and maintain data models for reporting, analytics, and AI consumption • Build AI-facing integration layers, including MCP servers and similar interfaces • Collaborate with AI, platform, and infrastructure teams on production-grade Agentic AI solutions • Implement data quality, testing, monitoring, observability, and lineage across pipelines and AI integrations • Apply data governance, privacy, security, and access-control standards • Prepare structured and unstructured data for analytics, machine learning, generative AI, and agentic workflows • Contribute to architecture decisions and scale the organization’s data platform

🎯 Requirements

• 4+ years of experience in Data Engineering, Backend Engineering, or a related field • Strong Python and SQL skills • Experience building and operating production data pipelines • Experience with cloud data platforms such as Snowflake, Databricks, BigQuery, Redshift, Microsoft Fabric, or similar • Experience with dbt or comparable data transformation tools • Experience with orchestration tools such as Airflow, Dagster, Prefect, Databricks Workflows, Azure Data Factory, or similar • Experience with dimensional modeling, SCDs, Data Vault, or comparable approaches • Experience with at least one major cloud provider: Azure, AWS, or GCP • Experience supporting, integrating, or developing AI-powered and Agentic AI solutions in production environments • Familiarity with RAG concepts, embeddings, indexing, and retrieval workflows • Experience with vector databases such as Pinecone, Weaviate, Qdrant, pgvector, or similar • Understanding of how AI agents consume data and interact with enterprise systems • Experience with CI/CD practices and version control workflows • Familiarity with Docker and containerized environments • Understanding of Infrastructure as Code, such as Terraform • Experience implementing monitoring, telemetry, dashboards, and alerting for production systems • Active use of AI-assisted development tools such as GitHub Copilot, Cursor, Claude Code, or similar • Excellent communication and collaboration skills • Ability to work with cross-functional engineering, platform, data, and AI teams • Comfortable working in an Agile Kanban environment • Professional English proficiency

🏖️ Benefits

• Market-based pay benchmarked to the candidate’s location • Automatic annual pay adjustments when the benchmark is higher than compensation • Compensation reviews for team members • Fair and equitable compensation practices • Full-time employment