Forward Deployed Analytics Engineer

Mexico City, Mexico City, Mexico

Arkham Technologies

Arkham transforms operations of enterprises in the Americas through exceptional Data & AI software.

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About Arkham

AI is revolutionizing how businesses operate, making processes more efficient and enabling data-driven decision-making. However, unlocking AI’s full potential requires clean, well-structured, and actionable data—and that’s where Arkham comes in.

At Arkham, we make AI adoption seamless with our AI Operating System, a suite of integrated Data & AI tools that helps businesses unify fragmented systems, streamline data processes, and deploy AI solutions up to 5X faster than traditional methods.

About the Role

Our implementation teams consist of two key roles: the Forward Deployed Analytics Engineer and the Forward Deployed Data Scientist. These roles work closely together to drive the implementation of Arkham’s AI Operating System, helping our customers transform their data and analytics capabilities in a matter of weeks.

As a Forward Deployed Analytics Engineer, you will be responsible for helping customers design and implement data models, analytics pipelines, and business intelligence solutions. Once a customer’s Data Platform is integrated with Arkham, you will work side by side with their teams—typically in finance, BI, or operations—to structure, transform, and activate their data for AI-driven insights. Example use cases include:

  • Designing & Implementing Data Models – Structuring data for efficient reporting and AI applications.
  • Optimizing Data Pipelines – Ensuring fast, scalable transformations to power analytics workflows.
  • Enabling Self-Service Analytics – Creating SQL-based transformations to empower teams with reliable, ready-to-use datasets.
  • Accelerating Business Intelligence – Integrating BI tools through Arkham's Platform.

This phase typically takes 2-4 weeks, during which you will fully implement the customer’s first analytics use case, ensuring that key pain points are addressed. By the end of this process, the customer’s business champion will have their “aha” moment, realizing the transformative power of Arkham’s AI Operating System. This success drives adoption and expansion across their organization.

You will play a critical role in customer success, managing 3-4 customer implementations at any given time and ensuring they maximize the value of their data and AI capabilities.

Requirements

Key Responsibilities

  • Data Modeling & Transformation – Build scalable, analytics-ready data models using Arkham’s Data Platform and Following the Medallion Architecture.
  • Pipeline Optimization – Work with data engineers to improve ETL/ELT workflows for analytics use cases.
  • Business Intelligence Enablement – Design dashboards, reports, and query-ready datasets for self-service analytics. 
  • Customer Collaboration – Work directly with business and technical teams to understand their data challenges and implement solutions.
  • Data Governance & Quality – Ensure data accuracy, consistency, and usability across use cases.
  • Performance Monitoring – Continuously track query performance, model execution times, and data freshness, making necessary improvements.
  • AI-Driven Analytics – Support AI-powered reporting, forecasting, and anomaly detection within customer workflows.

Qualifications

  • Experience: 3+ years in analytics engineering or data engineering.
  • SQL Expertise: Strong proficiency in SQL for data modeling and transformation.
  • Data Modeling: Experience designing dimensional models. Also knowledge of other techniques is preferred (i.e. Data Vault).
  • Python Skills: Basic proficiency for data automation and scripting.
  • Spark Expertise: Strong understanding of Spark’s architecture, execution model, and practical implementation for data processing and analytics.
  • Cloud & Data Warehousing: Familiarity with Snowflake, BigQuery, Redshift, or Databricks.
  • Customer-Facing Skills: Strong communication and collaboration abilities to work closely with clients.

Bonus Skills:

  • Knowledge of CI/CD practices for data workflows.
  • Experience with data observability and testing frameworks.
  • Familiarity with AI-driven analytics and Generative AI use cases.
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Tags: Architecture BigQuery Business Intelligence CI/CD Databricks Data governance Data pipelines Data Warehousing ELT Engineering ETL Finance Generative AI Pipelines Python Redshift Snowflake Spark SQL Testing

Region: North America
Country: Mexico

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