Data Engineer
Role details
Job location
Tech stack
Job description
We're looking for a customer-centric Senior AI/Data Engineer to build and scale data systems that drive our "decision intelligence" products. You'll work at the intersection of data engineering, product development, and AI/ML enablement-designing systems that empower customer-facing data products. This role requires not only deep technical expertise but also a strategic mindset, strong communication skills, and a high sense of ownership over how data supports the business. You will collaborate across teams to ensure data is a true product enabler. WHAT YOU'LL DO:
Engineering & AI Enablement
- AI/ML ownership: Build trusted AI/ML predictions and forecasts via feature pipelines, model input/output data flows, and robust data validation frameworks.
- Pipeline Design: Design and implement reliable, scalable, and secure data pipelines that serve analytical and product use cases.
- Leadership: Provide technical leadership and mentorship to other data engineers and cross-functional collaborators, fostering a culture of engineering excellence.
Ecosystem Ownership & Strategy
- Architecture & Evolution: Own the architecture and evolution of our data platform, ensuring it meets the performance, scalability, and agility needs of our growing business.
- Governance & Observability: Implement data governance, quality, and observability best practices to ensure trustworthy insights, proactively managing data health before it impacts the business.
- Infrastructure Optimization: Optimize cloud data infrastructure for cost, performance, and maintainability, treating the platform as a product rather than just a utility.
Collaboration & Translation
- Cross-Functional Partnership: Collaborate closely with product managers, engineers, and customer stakeholders to understand context and needs, and help translate them into engineering solutions.
- Customer-Centricity: Ensure engineering efforts are aligned with delivering clear business value and enhancing the customer experience., As a Senior AI/Data Engineer, you will design and maintain scalable data and AI infrastructures, partnering with cross-functional teams to deliver reliable data systems and support AI-driven business capabilities. Top Skills: AirflowAWSAws GlueAws Step FunctionsCi/CdCircleCIDagsterDbtEcsEmrGithub ActionsGlue CatalogLake FormationLambdaPysparkPythonS3SnowflakeSparkSQLSqsTerraform G2i
Data Engineer
13 Days Ago In-Office or Remote Mid level Mid level HR Tech * Other * Professional Services The Senior AI/Data Engineer will develop AI workflows for document analysis and qualification, create backend APIs, and ensure system reliability and performance. Top Skills: Azure Blob StorageAzure OpenaiDockerFastapiGitPostgresPrefectPydanticPythonSqlalchemy GE Aerospace, The Senior Data Engineer designs AWS-native data foundations for AI applications, focusing on knowledge graphs, data quality, and data governance. Responsibilities include data modeling, pipeline building, and mentoring other engineers. The role requires collaboration across teams to integrate data access with security policies and improve AI system effectiveness. Top Skills: AthenaAWSCloudFormationCypherGlueGremlinLambdaNeo4JNeptuneOpensearchPythonRdf/SparqlS3SQLStep Functions
What you need to know about the Colorado Tech Scene
With a business-friendly climate and research universities like CU Boulder and Colorado State, Colorado has made a name for itself as a startup ecosystem. The state boasts a skilled workforce and high quality of life thanks to its affordable housing, vibrant cultural scene and unparalleled opportunities for outdoor recreation. Colorado is also home to the National Renewable Energy Laboratory, helping cement its status as a hub for renewable energy innovation.
Key Facts About Colorado Tech
- Number of Tech Workers: 260,000; 8.5% of overall workforce (2024 CompTIA survey)
- Major Tech Employers: Lockheed Martin, Century Link, Comcast, BAE Systems, Level 3
- Key Industries: Software, artificial intelligence, aerospace, e-commerce, fintech, healthtech
- Funding Landscape: $4.9 billion in VC funding in 2024 (Pitchbook)
- Notable Investors: Access Venture Partners, Ridgeline Ventures, Techstars, Blackhorn Ventures
- Research Centers and Universities: Colorado School of Mines, University of Colorado Boulder, University of Denver, Colorado State University, Mesa Laboratory, Space Science Institute, National Center for Atmospheric Research, National Renewable Energy Laboratory, Gottlieb Institute
Requirements
- The ML Enabler: You are a Data Engineer who loves the complexity of AI. You understand that a model is only as good as the pipeline feeding it, and you take pride in building the infrastructure that brings AI to life.
- The Product-Minded Architect: You don't just move data from A to B; you build systems with the end-user in mind. You prioritize "Time to Insight" and usability as much as you prioritize code efficiency.
- The Strategic Owner: You are comfortable working in an environment where you are expected to identify problems and fix them without waiting for a ticket. You view the data ecosystem as your product.
- We know that great talent comes from many backgrounds. If you are a builder who cares about the "why" behind the code, we want to hear from you!
WHAT YOU'LL BRING:
- Experience: 5+ years of experience in data engineering, with 1-2+ years in a senior or lead capacity.
- Deep Technical Expertise: You possess a profound understanding of ML models and can articulate the trade-offs between different architectures (e.g., complexity vs. inference speed, accuracy vs. interpretability) to ensure the right tool is selected for the job.
- Coding: "Ninja-level" proficiency in Python for complex data structures and automation, alongside strong SQL expertise.
- ML Frameworks: Strong familiarity with Scikit-Learn and similar libraries, with specific experience building and maintaining associated feature engineering pipelines.
- Ops & Orchestration: High proficiency in MLOps practices and orchestration tools (e.g., Airflow, dbt, Dagster) to manage model lifecycles and data dependencies.
- Modern Data Stack: Solid experience with modern data platforms (e.g., Snowflake, BigQuery, Redshift, or Databricks).
- Cloud & Modeling: Strong understanding of data modeling, performance optimization, and cloud computing (AWS, GCP, or Azure).
- Communication: Excellent communication and collaboration skills, with a proven ability to work effectively across technical and non-technical teams.
Benefits & conditions
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Employees can expect a robust benefits package, including health and dental and 401k with company match AND BEYOND...
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Find your perfect work/life balance with our Flexible Time Off policy or generous PTO plan (role dependent) and paid holidays
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Up to 4 weeks paid bonding leave
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Tuition reimbursement
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Robust Employee Assistance Program through TotalCare offering free counseling 24/7/365, plus financial counseling, legal guidance, adoption assistance services and much more!
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24/7 access to virtual medical care with Teladoc
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Quarterly awards based on peer nominations
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Regional discounts and perks
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Opportunities to participate in charitable events and give back to the community GROW WITH US:
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We understand the impact of attracting and keeping top talent and reward intellectual curiosity and a thirst for personal and professional growth
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Encouraging our employees that already have an intimate knowledge of and passion for our products to apply for other roles within our walls just makes sense!