Analytics Engineer

The Senior Analytics Engineer provides advanced analytics and data engineering support across multiple business and program areas. This role sits at the intersection of data engineering, analytics, and business intelligence—designing scalable data pipelines and analytics-ready datasets while delivering dashboards and analytical models that drive data-informed decisions and operational efficiency.

This position is fully remote and requires a Public Trust (or the ability to obtain it). US citizenship required. The candidate may be required to work outside of business hours, including weekends, based on need.

Key Responsibilities

• Design, build, and maintain automated, scalable ETL/ELT data pipelines using Python, SQL, and cloud-based tools to integrate, transform, and validate structured and unstructured data from diverse sources.
• Develop and manage analytics-ready data models and workflows (e.g., in Databricks or similar platforms) to support reporting, self-service analytics, and advanced data science use cases.
• Implement CI/CD practices using GitLab for data workflows, ensuring reliable, versioned, and repeatable analytics and data engineering processes.
• Design, develop, and deploy interactive dashboards and reports using Tableau, Power BI, or similar tools to deliver complex analysis and actionable insights to business and technical stakeholders.
• Perform data mining, cleaning, and manipulation using SQL and Python (e.g., Pandas, NumPy) to support statistical analyses, visualizations, and decision-support tools.
• Conduct end-to-end analytical and modeling work, including exploratory data analysis, feature preparation, model validation, and documentation; experience with AI or predictive modeling is a plus.
• Collaborate with cross-functional teams (data engineers, analysts, software developers, and stakeholders) to translate business requirements into effective data models, pipelines, and visualizations.
• Compile and maintain metadata, data dictionaries, and technical documentation; produce recurring and ad-hoc reports for leadership.
• Respond to urgent and ad-hoc data requests and support collaborative research and analysis projects across program areas.
• Provide technical guidance and mentorship on analytics best practices, Python scripting, data modeling, and workflow automation.

Required Qualifications

• Bachelor’s degree in data science, Computer Science, Statistics, Mathematics, Engineering, or a related quantitative field, with 5+ years of experience (or 3+ years with a Master's) in analytics engineering, data engineering, or data analysis.
• Strong proficiency in Python, SQL, Git/GitLab, and experience building ETL/ELT pipelines with CI/CD and data engineering best practices.
• Experience working with relational and non-relational databases (e.g., Oracle, PostgreSQL) and creating executive-ready dashboards using Tableau or Power BI.
• Strong analytical and problem-solving skills, attention to detail, and the ability to analyze large, complex datasets and communicate insights to technical and non-technical stakeholders.
• Ability to work independently and collaboratively in fast-paced, agile environments with excellent written and verbal communication skills.

Preferred Qualifications

• Experience with Databricks, cloud platforms (especially AWS), and modern data infrastructure.
• Exposure to AI/ML, advanced data modeling (classification, forecasting, NLP), and MLOps practices.
• Familiarity with workflow orchestration and automation tools such as Airflow, MLflow, or similar platforms.
• Experience working with government or regulated data environments, Agile/Scrum methodologies, and project management tools like Jira.
• Experience mentoring junior data professionals and contributing to analytics standards, best practices, and team development.