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Sr. Data Analytics Engineer

IBM
Department:Product Design
Type:REMOTE
Region:UK
Location:Cambridge, England, United Kingdom
Experience:Mid-Senior level
Estimated Salary:£70,000 - £100,000
Skills:
SQLDBTSNOWFLAKEREDSHIFTPYTHONRDATA MODELINGDATA WAREHOUSINGDATA LAKE
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Job Description

Posted on: August 17, 2025

Introduction A career in IBM Software means you'll be part of a team that transforms our customer's challenges into industry-leading solutions. We are an infinitely curious team, always seeking new possibilities, and dedicated to creating the world's leading AI-powered, cloud-native software solutions. Our renowned legacy creates endless global opportunities for our network of IBMers. We are a team of deep product experts, ensuring exceptional client experiences, with a focus on delivery, excellence, and obsession over customer outcomes. This position involves contributing to HashiCorp's offerings, now part of IBM, which empower organizations to automate and secure multi-cloud and hybrid environments. You will join a team managing the lifecycle of infrastructure and security, enhancing IBM's cloud solutions to ensure enterprises achieve efficiency, security, and scalability in their cloud journey. Recruiter should collaborate to create the relevant verbiage. Your Role And Responsibilities This is an individual contributor role responsible for hands-on development. Analytics Engineering & Enablement is focused on translating our trove of data into consumable models and training that accelerate time-to-insight for the HashiCorp product organization. As a Sr. Analytics Engineer, you will work closely with subject matter experts to design and develop data models that power common analytics use cases, powering ad hoc analyses, reports/dashboards, and AI/ML models. You will make it easier for every PM and key business stakeholder at HashiCorp to consistently self-serve answers to their questions about customer behavior and product usage, by leveraging the wealth of customer and product data we generate. What You’ll Do (responsibilities)

  • Own the creation and maintenance of self-serve tools (data models, reports, dashboards, etc.) that enable business stakeholders to be data-driven in their decision-making.
  • Deliver on inquisitive analyses that answer critical business questions.
  • Develop training and enablement content to ramp users on models and dashboards in our portfolio.
  • Identify and drive opportunities to make data more consumable and decrease time-to-insight for analytics consumers.
  • Reduced time required for self-service users to generate actionable insights.
  • High adoption of data models and dashboards you create.
  • Reliable, accurate, and well-documented data solutions that stakeholders trust.

This job can be performed from anywhere in the US Preferred Education Bachelor's Degree Required Technical And Professional Expertise

  • Reduced time required for self-service users to generate actionable insights.
  • High adoption of data models and dashboards you create.
  • Reliable, accurate, and well-documented data solutions that stakeholders trust.
  • Bachelor’s degree in Computer Science, Data Science, Engineering, or a related field (or equivalent experience).
  • 7+ years of experience in analytics, analytics engineering, data engineering, or a related role.
  • Solid grasp of SQL, structuring data, and ability to effectively query multiple tables of different grains with the use of subqueries/ctes and window functions.
  • Proficiency in data modeling tools like dbt or similar frameworks.
  • Experience working with modern data management systems (e.g. Snowflake, Redshift, etc.).

Preferred Technical And Professional Experience

  • Ability to shape ambiguity into tangible deliverables.
  • Excellent problem-solving analytical skills with the ability to synthesize and communicate complex results.
  • Demonstrated communication skills with an emphasis on written communication
  • Comfortability with programming languages (e.g. Python, R, etc.) for data manipulation and automation.
  • Knowledge of visualization best practices.
  • Solid grasp of data modeling and semantic layers.
  • Understands both data lake and data warehousing approaches to data management.
Originally posted on LinkedIn

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