Location: US Remote

Reports To: Director of Data and Analytics 

Salary Range: $130,000 - $170,000

Position Summary:

The Data Engineer is a core builder on IEM’s data and analytics team. This role develops and maintains the data pipelines and transformation models that power Tableau dashboards and business decisions across the organization. Working within an established modern data stack (Fivetran, Snowflake, dbt, Tableau. The Data Engineer turns raw source data into reliable, well-tested, and well-documented analytics-ready datasets. This is a hands-on individual contributor role with real ownership of production data models and growing influence on engineering standards.

Ideal Candidate Profile

You have 3–5 years of experience building data pipelines and transformation workflows in cloud environments. You’re proficient with SQL and Python, comfortable working in Snowflake, and have hands-on experience with dbt or similar transformation tools. You write clean, tested code and take pride in documentation. You’re curious about the business context behind the data and can translate stakeholder questions into well-modeled datasets. You work well on a small team, take feedback constructively, and are eager to grow your skills in analytics engineering, data modeling, and cloud data architecture. You’re excited about AI’s role in modern data work and comfortable using AI coding assistants and agents to accelerate your output.

Key Responsibilities:

• Data Pipeline Development: Build and maintain ELT pipelines using Fivetran and custom integrations that ingest data from source systems including Procore, Salesforce, ERP platforms, and internal databases into Snowflake
• dbt Transformation Models: Develop, test, and document dbt models that transform raw data into clean, reliable datasets for analytics and reporting across Finance, Production, Supply Chain, and Engineering
• Data Modeling: Build dimensional models and staging layers following team conventions, ensuring data is structured for optimal Tableau dashboard performance and ad-hoc analysis
• Data Quality: Write and maintain dbt tests, monitor data freshness, and investigate data quality issues when they arise, owning resolution through to root cause
• Source System Integration: Work with APIs and data connectors to integrate new data sources, troubleshoot ingestion issues, and ensure reliable data flow into the warehouse
• Documentation: Maintain clear documentation for data models, pipeline configurations, and business logic so the team can understand and extend your work
• Collaboration: Partner with business stakeholders to understand data needs, clarify requirements, and deliver datasets that answer real operational questions
• Performance: Monitor query performance and pipeline efficiency, identifying opportunities to optimize warehouse costs and model run times
• Engineering Standards: Participate in code reviews, follow Git workflows and CI/CD practices, and contribute to improving the team’s development processes
• Continuous Learning: Stay current with modern data stack tools and practices, bringing ideas for improvement back to the team
• AI-Assisted Development: Use AI coding assistants and agent-based tools to accelerate pipeline development, code generation, testing, and documentation. Manage AI agents as part of your daily workflow to increase throughput and quality

Qualifications:

• Bachelor’s degree in Computer Science, Information Systems, Data Science, or related field, or equivalent experience
• 3–5 years of experience in data engineering, analytics engineering, or BI development with hands-on experience building production data pipelines
• Strong SQL skills including CTEs, window functions, complex joins, and query optimization
• Experience with Snowflake or similar cloud data warehouses
• Working experience with dbt (data build tool) for building and testing data transformation workflows
• Proficiency in Python for data processing, scripting, and API integrations
• Experience with data integration platforms such as Fivetran or similar ELT tools
• Familiarity with Tableau or similar BI tools and understanding of how data structure impacts dashboard performance
• Comfortable with Git version control and modern development workflows including code review and CI/CD
• Strong problem-solving skills with ability to debug data issues systematically
• Clear written and verbal communication skills with ability to document work and explain technical concepts to non-technical stakeholders
• Self-motivated with ability to work independently in a remote environment while collaborating across a distributed team
• Experience using AI coding assistants (e.g., Claude, GitHub Copilot) and comfort directing AI agents to perform data engineering tasks such as writing SQL, generating dbt models, and debugging pipeline issues
• Preferred: Experience with manufacturing, construction, or project management data systems such as Procore, ERP platforms, or supply chain tools
• Preferred: Familiarity with dimensional modeling concepts (star schemas, fact/dimension tables)

Location

Fully remote within the United States. May require up to 10% travel to IEM facilities for team collaboration, project kickoffs, and stakeholder meetings. 

The Role 

We are seeking a Senior ing functions within the First Line of Defense. This role will primarily focus on monitoring and analyzing Fraud Key Risk Indicators (KRIs), Risk Appetite Statement (RAS) metrics, Strategy and Rule monitoring oversight, governance documentation, and other enterprise fraud 

performance metrics to ensure the Fraud Program operates within defined risk tolerances and governance expectations. 

The Senior Risk Analyst will play a critical role in governance control, analyzing fraud performance trends, identifying emerging risk signals, supporting escalation of metric breaches, and delivering data-driven insights to senior leadership and enterprise risk committees. This position will also support audit, regulatory examination, and fraud risk assessment activities, helping ensure the Fraud Program maintains strong governance, transparency, and regulatory alignment. 

While the primary focus of this role is fraud risk analytics and performance monitoring, the Senior Analyst will also assist the broader Fraud Program team in supporting program governance, documentation, strategy oversight, and cross-functional coordination across the Fraud ecosystem. 

The ideal candidate will bring strong analytical capabilities, experience with risk assessment frameworks or audit processes, a deep understanding of fraud risk management within a banking or fintech environment, and the ability to drive cross-functional collaboration between all lines of business. 

What you’ll do: 

Fraud Risk Metrics Monitoring & Performance Analytics (Primary Focus) 

• Monitor and analyze Fraud Key Risk Indicators (KRIs), Risk Appetite Statement (RAS) metrics, and other enterprise fraud performance indicators across products and business lines. ● Identify performance trends, emerging risks, and early warning signals across fraud domains including account takeover, transaction fraud, disputes, and fraud operations performance.
• Support the escalation and investigation of metric breaches, working with cross-functional teams to determine root causes and corrective actions. 
• Produce regular risk reporting and executive summaries for fraud governance committees and leadership forums. 
• Develop and maintain data-driven insights and dashboards to improve transparency into fraud risk performance across the enterprise. 

Fraud Risk Governance & Oversight 

• Assist with maintaining and enhancing the Fraud Program’s KRI and KPI framework, ensuring alignment with enterprise risk management and regulatory expectations. 
• Support ongoing metric validation, threshold monitoring, and governance processes to ensure fraud metrics remain accurate, stable, and meaningful. 
• Partner with Fraud Strategy, Operations, Data, and Risk teams to ensure consistent measurement and interpretation of fraud risk performance across products. 

Risk Assessment, Audit & Regulatory Support 

• Assist in the execution of Fraud Risk Self-Assessments (FRSAs) and other targeted risk evaluations across fraud domains through data analytic needs. 
• Support the tracking and remediation of fraud-related issues identified through audit, risk assessments, regulatory reviews, or control testing. 
• Coordinate with Fraud Program Lead to support regulatory exam responses and documentation requests. 

Fraud Program Governance & Documentation (Supporting Role) 

• Assist in maintaining fraud program documentation, procedures, and governance artifacts, including playbooks, policies, and operational standards. 
• Support coordination of fraud governance forums and committee reporting, ensuring accurate and timely risk reporting. 
• Assist with cross-functional initiatives and program management efforts that support the overall fraud risk management ecosystem. 

What you’ll need: 

• 5+ years of experience in fraud analytics, risk management, or fraud program management.
• Strong experience in leading projects, working cross-functionally, and influencing senior stakeholders. 
• Experience working with multiple cross-functional teams to deliver results 
• Experience in banking and/or fintech industry 
• Strong Knowledge of fraud prevention tools and techniques. 
• Excellent problem-solving skills and ability to translate complex data into clear business recommendations. 
• Strong proficiency in SQL, experience with visualization tools (e.g., Tableau) ● Familiarity with Python. 
• Strong experience creating scalable playbooks, reporting templates, and spreadsheet-based workflows to improve risk management processes and cross-functional coordination.
• Highly self-directed, with demonstrated ability to think strategically, solve ambiguous problems, and independently drive initiatives from ideation through implementation. 
• Strong presentation and executive communication skills, including developing clear, concise materials for senior leadership and regularly presenting insights, recommendations, and progress updates. 

Nice to Have: 

• Experience with Model Risk Management or Model Governance, a plus! 
• Advanced working knowledge of Python for process automation, including writing maintainable code, testing solutions independently, and preparing them for production. ● Working knowledge of GitLab, DBT, and Airflow, including the ability to independently deploy, schedule, and maintain analytical workflows in production. 
• Proficient in Airtable, including designing automations and workflow trackers to be leveraged across multiple product teams.