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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Analytics Engineer - **Company:** NContracts, LLC - **Location:** United States - **Experience:** Expert - **Salary:** $120,000.0 - $160,000.0 - **Contract:** Temporary contract - **Skills:** Query Performance, A/B Testing, Artificial Intelligence, Airflow, Data Analysis, BigQuery, Cloud Computing, Code Review, Continuous Integration, Data Architecture, Data Governance, Extract Transform Load (ETL), Data Transformation, Data Visualization, Database Queries, Dimensional Modeling, JSON, Performance Tuning, Standard Sql, Systems Architecture, Scripting, Delivery Pipeline, Snowflake, Git, Semi-structured Data, Information Technology, Data Analytics, Operational Systems, Software Coding, Software Version Control, Amazon Redshift, Databricks - **Published:** May 19, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=2b4fc9074f486fdc ## About the Role Technical Foundation * 5+ years of experience in analytics engineering, data analytics, or a related data role with demonstrated technical expertise * Strong SQL proficiency with experience writing complex queries for data transformation, analysis, and performance optimization * Hands-on experience with dbt (dbt Core or dbt Cloud), including model design, testing, documentation, and deployment workflows * Experience working with Snowflake or a comparable cloud data warehouse (BigQuery, Redshift, Databricks) * Solid understanding of data modeling concepts, including dimensional modeling, slowly changing dimensions, and star/snowflake schemas * Proficiency with data visualization and BI tools, preferably Sigma, with the ability to create compelling dashboards and reports for diverse audiences * Understanding of software engineering fundamentals: version control (Git), testing, code review, and CI/CD practices * Comfort working with semi-structured data (JSON, nested structures) * Bachelor's degree in Computer Science, Data Science, Statistics, Mathematics, or equivalent practical experience Analytical Mindset * Experience building metrics frameworks and defining KPIs that drive product and business decisions * Track record of error analysis and root cause investigation on product and data quality issues * Ability to work backwards from business questions to data requirements * Healthy skepticism about data quality and metric definitions-you ask "should we trust this number?" before building on it Product Orientation * Genuine curiosity about how products work and why users behave the way they do * Experience partnering directly with product and engineering teams in a collaborative, cross-functional environment * Communication skills to make technical findings accessible to non-technical stakeholders * Comfort with ambiguity and the ability to iterate toward the right answer rather than waiting for perfect requirements BONUS POINTS (Preferred Qualifications) * Knowledge of financial services data domains such as lending, compliance, risk management, or regulatory reporting * Background in high-volume event data, behavioral analytics, or product telemetry systems * Exposure to funnel analysis, cohort analysis, or experimentation frameworks (A/B testing) * Understanding of software system architectures and how they generate telemetry * Enthusiasm for AI and a habit of using AI tools to accelerate your own work-writing code, automating tasks, exploring data, or prototyping solutions * Experience measuring or evaluating AI-powered product features, including defining success metrics, tracking adoption, or analyzing model performance from a product analytics perspective * Familiarity with emerging data and workflow patterns, including tracing, observability, and agent-based systems * Experience with Python for data transformation, scripting, or automation tasks * Experience with data observability platforms (Monte Carlo, Elementary, Soda) * Knowledge of compliance frameworks relevant to financial services (SOC 2, GLBA, FFIEC) * Experience implementing data contracts, data mesh, or modern data governance frameworks * Exposure to orchestration tools such as Airflow, Dagster, or Prefect * dbt Analytics Engineering Certification or SnowPro certification * Familiarity with reverse ETL tools (Census, Hightouch) for activating data in operational systems * Contributions to open-source data or analytics projects ## Description We are currently seeking an Analytics Engineer to join our growing team! The Analytics Engineer reports to the Data Architect and is responsible for building the data models and analytical frameworks that drive product improvement across Ncontracts' suite of risk management and compliance solutions. This role sits at the intersection of data modeling and product analytics. You'll own the transformation and presentation layers that turn raw product data into actionable insights, working closely with engineering and product teams to identify what's working, what isn't, and why. You'll need strong SQL and modeling skills to build reliable, well-tested datasets, and equally strong product intuition to know which metrics actually matter for our financial institution customers. Our modern data stack includes Snowflake as our cloud data warehouse, dbt for data transformation and modeling, and Sigma for business intelligence and visualization. You'll own the modeling layer end-to-end-designing well-structured, documented, and tested datasets that power decisions across the organization. This role will have a direct impact on our customers and our brand. You'll deliver the next generation of customer-facing reporting, dashboards, and data products that elevate the Ncontracts experience and set a new standard for how financial institutions interact with their data. We're looking for candidates who are curious, resourceful, and genuinely excited about AI-whether that's using it to streamline their own workflows, finding smarter ways to validate data, or simply staying on top of how the landscape is evolving. You don't need to be an ML engineer, but we want someone who leans into new tools and approaches rather than waiting to be told to use them. YOU WILL * Design, build, and maintain the data models in Snowflake using dbt that power product analytics-from raw source data through to business-ready, well-documented datasets * Define and maintain metrics frameworks and KPIs that drive product and business decisions across the organization * Build interactive dashboards and self-service reports in Sigma that empower stakeholders to explore data and answer their own questions * Design instrumentation strategies with engineering teams to ensure we're capturing the right signals from Ncontracts' product suite * Partner with product managers to translate fuzzy questions ("why is this feature underperforming?") into precise, answerable analyses * Conduct deep-dive analyses including funnel analysis, cohort analysis, and root cause investigations to surface product insights * Own data quality end-to-end, including dbt tests, validation, documentation, and monitoring across all models * Optimize query performance and warehouse costs in Snowflake through efficient modeling patterns, materialization strategies, and resource monitoring * Establish and enforce data governance standards, including naming conventions, documentation requirements, and consistent metric definitions to maintain a single source of truth * Contribute to the evolution of the data platform by evaluating new tools, technologies, and best practices ## Related Videos - [From Messy Queries to Scalable Systems - How Data Engineering actually works](https://www.wearedevelopers.com/videos/100203-from-messy-queries-to-scalable-systems-how-data-engineering-actually-works) - [Tips and Tricks for Working with JSON](https://www.wearedevelopers.com/videos/1229-tips-and-tricks-for-working-with-json) - [How a Small Team Shrank a Microsoft Monorepo by 94%](https://www.wearedevelopers.com/videos/1236-how-a-small-team-shrank-a-microsoft-monorepo-by-94) - [Enjoying SQL data pipelines with dbt](https://www.wearedevelopers.com/videos/823-enjoying-sql-data-pipelines-with-dbt) - [AI Model Management Life Circles: ML Ops For Generative AI Models From Research to Deployment](https://www.wearedevelopers.com/videos/1152-ai-model-management-life-circles-ml-ops-for-generative-ai-models-from-research-to-deployment) - [Introducing JSON Structure](https://www.wearedevelopers.com/videos/100219-introducing-json-structure) ## Related Articles - [Making Data Warehouses Fast: A Developer’s Story](https://www.wearedevelopers.com/magazine/107-making-data-warehouses-fast-a-developer-s-story) - [Data Engineer Salary UK](https://www.wearedevelopers.com/magazine/253-data-engineer-salary-uk) - [Data Analyst Salary in the UK](https://www.wearedevelopers.com/magazine/278-data-analyst-salary-in-the-uk) - [Highest Paying Tech Companies for Developers](https://www.wearedevelopers.com/magazine/220-highest-paying-tech-companies-for-developers) - [Graph and AI Trends 2026: Why Is AI Running but Not Yet Delivering?](https://www.wearedevelopers.com/magazine/680-graph-and-ai-trends-2026-why-is-ai-running-but-not-yet-delivering) - [Coffee with Developers - Maria Apazoglou - Making AI understandable for all in production](https://www.wearedevelopers.com/magazine/475-coffee-with-developers-maria-apazoglou-making-ai-understandable-for-all-in-production)