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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.

Average salary estimate

$105000 / YEARLY (est.)
min
max
$90000K
$120000K

If an employer mentions a salary or salary range on their job, we display it as an "Employer Estimate". If a job has no salary data, Rise displays an estimate if available.

What You Should Know About Forward Deployed Analytics Engineer, Arkham Technologies

Join Arkham as a Forward Deployed Analytics Engineer and immerse yourself in the world of advanced data and AI solutions! At Arkham, we pride ourselves on our innovative Data & AI Platform, which has become essential for industry leaders such as Circle K and Televisa Editorial. As a Forward Deployed Analytics Engineer, you will take the reins in designing and implementing data models, analytics pipelines, and business intelligence solutions that revolutionize how our customers leverage their data. Your collaborative spirit will shine as you partner directly with finance, operations, and BI teams to transform their data into actionable insights using our powerful platform. You'll be engaging with customers for about 2 to 4 weeks on each project to ensure smooth integration, addressing key pain points, and facilitating their journey towards AI-driven analytics. With responsibilities that include optimizing data pipelines for swift transformations and empowering teams with self-service analytics, you'll play a crucial role in customer success, managing multiple implementations simultaneously. If you have a knack for data modeling, a strong foundation in SQL, and a passion for helping organizations unlock their data potential, then we want to hear from you. At Arkham, your contributions are not just about technical prowess but also about fostering relationships and delivering tangible results that lead to those 'aha' moments with our clients. Become a critical part of a dynamic team that’s dedicated to driving transformative strategies in data and AI!

Frequently Asked Questions (FAQs) for Forward Deployed Analytics Engineer Role at Arkham Technologies
What are the key responsibilities of a Forward Deployed Analytics Engineer at Arkham?

As a Forward Deployed Analytics Engineer at Arkham, your responsibilities will include building scalable data models, optimizing ETL/ELT workflows, designing self-service analytics dashboards, collaborating with customer teams to understand their data challenges, ensuring data governance and quality, monitoring performance, and supporting AI-driven analytics. You'll be integral to helping customers maximize their data's value and capabilities.

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What qualifications are required for the Forward Deployed Analytics Engineer position at Arkham?

Arkham is looking for candidates with over 3 years of experience in analytics or data engineering, strong SQL skills for data modeling, experience in designing dimensional models, basic proficiency in Python for automation, and expertise in Spark. Familiarity with cloud data warehousing solutions like Snowflake or BigQuery is also essential.

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How does a Forward Deployed Analytics Engineer contribute to customer success at Arkham?

In the role of Forward Deployed Analytics Engineer at Arkham, you will manage multiple customer implementations, ensuring that each client achieves significant breakthroughs in their data utilization. Your hands-on guidance in constructing data models and analytics pipelines directly leads to enhanced insights and operational efficiencies, enabling customers to realize the full potential of Arkham's Data & AI Platform.

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What tools and technologies will a Forward Deployed Analytics Engineer use at Arkham?

As a Forward Deployed Analytics Engineer at Arkham, you will be working with a variety of tools and technologies, including SQL for data modeling, Python for scripting and automation, and Spark for data processing. Familiarity with cloud platforms and data warehouses like Snowflake, BigQuery, and Redshift will be essential for optimizing data workflows.

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What makes Arkham a great place to work as a Forward Deployed Analytics Engineer?

Arkham embraces a culture of innovation and collaboration, making it an ideal workplace for a Forward Deployed Analytics Engineer. You'll have the opportunity to directly impact customer success by solving complex data challenges with cutting-edge technology in a supportive and dynamic team environment. Additionally, our focus on continuous learning and professional growth ensures you’ll be advancing your skills in an exciting field.

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Common Interview Questions for Forward Deployed Analytics Engineer
Can you describe your experience with data modeling as a Forward Deployed Analytics Engineer?

In answering this question, emphasize projects where you designed data models to support analytics. Discuss specific techniques you've applied, such as dimensional modeling or Data Vault, and share how these models have enabled effective reporting and insights.

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How do you optimize data pipelines, and what tools do you use?

Describe your approach to pipeline optimization, focusing on performance monitoring, transformation techniques you have implemented, and any tools like Apache Spark you've used. Include examples of how your optimizations increased efficiency or reduced processing times.

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What strategies do you use for customer engagement in data analysis projects?

Talk about your approach to understanding customer needs; ask open-ended questions to gather requirements, how you build rapport, and ensure that client expectations are aligned with delivery timelines. Share specific examples of successful engagement and outcomes.

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Tell us about a challenging project where you struggled with data quality. How did you address it?

Share a specific instance where you encountered data quality issues, detailing the steps you took to identify, rectify, and prevent future occurrences. Highlight collaboration with other teams to improve data governance and ensure consistent data accuracy.

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What is your experience with AI-driven analytics?

Discuss specific AI-driven projects you have been a part of, including the types of models used and how they've been integrated into reporting or operational workflows. Illustrate your understanding of both the technical and business implications of AI in analytics.

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How would you approach enabling self-service analytics for a client?

Explain your planned engagement with client teams to assess their current capabilities, identify training needs, and create intuitive dashboards that empower users. Discuss documentation and support strategies you would implement to ensure ongoing success.

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What data warehousing solutions are you familiar with, and how have you used them?

Mention specific cloud platforms you've worked with, such as Snowflake or BigQuery, and describe how you've utilized these solutions in building or optimizing data warehouses. Include examples of projects where these tools led to improved performance or insights.

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How do you ensure data accuracy and consistency across different use cases?

Talk about the data governance practices you follow, including data validation techniques, monitoring for anomalies, and setting up quality assurance protocols. Share outcomes from implementing these strategies to maintain high data standards.

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Can you explain your experience with SQL, especially in a business intelligence context?

Be ready to discuss your proficiency in SQL, including writing complex queries for data analysis, transforming datasets for BI tools, and any relevant experiences with reporting tools. Provide examples of how your SQL skills have led to actionable insights for business stakeholders.

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What methodologies do you use to monitor and improve performance in data models?

Discuss techniques like profiling query performance, checking execution times, and making iterative improvements based on user feedback. Include any tools or metrics you find essential in your performance monitoring workflow.

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DATE POSTED
March 19, 2025

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