Forward Deployed Analytics Engineer

About Arkham

Arkham is a Data & AI Platform—a suite of powerful tools designed to help you unify your data and use the best Machine Learning and Generative AI models to solve your most complex operational challenges.

Today, industry leaders like Circle K, Mexico Infrastructure Partners, and Televisa Editorial rely on our platform to simplify access to data and insights, automate complex processes, and optimize operations. With our platform and implementation service, our customers save time, reduce costs, and build a strong foundation for lasting Data and AI transformation.

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 Data & AI Platform, 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 Data & AI Platform. 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.

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.