Lead Data Analyst

Saab•East Syracuse, NY

2d•$115,700 - $165,500•Remote

About The Position

The Lead Data Analyst is a senior individual contributor responsible for driving data-informed decision-making across the organization. This role leads complex analytical projects from discovery through delivery, mentors junior analysts, and partners with stakeholders across business units to deliver insights that directly inform strategy and operations. The ideal candidate brings deep technical expertise, strong business acumen, and exceptional communication skills.

Requirements

Bachelor’s degree in Statistics, Mathematics, Computer Science, Economics, or a related quantitative field with 5+ years of experience in a data analyst, business intelligence, or related analytical role, or a Master's degree with 3+ years of experience, or an equivalent combination of education and experience.
Advanced proficiency in SQL and experience working with large-scale data warehouses such as Snowflake, BigQuery, Redshift, or similar platforms.
Hands-on experience with at least one BI or visualization tool, including Tableau, Power BI, Looker, or equivalent.
Strong working knowledge of Python or R for data manipulation, statistical analysis, and automation.
Understanding of Azure services including Azure Synapse Analytics, Azure Machine Learning, Azure Databricks, Azure SQL Database, Azure Data Lake Storage, Azure Blob Storage, Azure Data Factory, and Azure Logic Apps.
Demonstrated experience leading analytical projects independently from problem definition through stakeholder delivery.
Exceptional written and verbal communication skills, with the ability to present complex data to executive audiences.
Must be a U.S. citizen.
Applicants selected may be subject to a government security investigation and must meet eligibility requirements for access to classified information.
As a condition of employment, candidates will be required to pass a pre-employment drug screen.

Nice To Haves

Experience with dbt, Airflow, or other data transformation and orchestration tools.
Familiarity with experimentation platforms and A/B testing methodologies.
Background in machine learning or predictive modeling using tools such as scikit-learn, XGBoost, or similar frameworks.
Knowledge of data governance practices and exposure to data cataloging tools such as Alation or Collibra.

Responsibilities

Lead end-to-end analytical projects, including scoping, data collection, modeling, and presentation of findings to senior leadership.
Serve as the primary analytics point of contact for assigned business domains, such as marketing, product, finance, or operations.
Define and maintain KPIs, dashboards, and reporting frameworks that align with organizational objectives.
Translate ambiguous business questions into structured analytical problems with clear deliverables.
Design and execute advanced analyses including statistical modeling, forecasting, cohort analysis, segmentation, and A/B testing.
Build and maintain complex SQL queries, data pipelines, and analytical datasets in cloud data warehouse environments.
Develop predictive models and apply machine learning techniques where appropriate to drive business value.
Ensure data quality, integrity, and consistency across all analytical outputs.
Create and manage executive-level dashboards and self-service reporting tools using BI platforms such as Tableau, Power BI, and Looker.
Present findings and communicate insights to technical and non-technical audiences in a clear, compelling, and actionable manner that influences decision-making across all levels of the organization.
Document analytical methodologies, data definitions, and business logic to support knowledge sharing.
Mentor and coach junior and mid-level analysts, including conducting code reviews and providing technical guidance.
Partner cross-functionally with Data Engineering, Product, Finance, and Marketing teams to align on data strategy and priorities.
Contribute to team best practices, including coding standards, peer review processes, and analytical documentation.
Apply strong analytical thinking to break down complex problems systematically and draw evidence-based conclusions.
Connect analytical outputs to tangible business outcomes and strategic goals through strong business acumen.
Maintain high standards for code quality, reproducibility, and documentation.
Invest in the growth and development of peers and junior team members through mentorship and knowledge sharing.
Demonstrate ownership and initiative by proactively identifying opportunities and driving projects to completion with minimal supervision.
Travel up to 15% annually, both domestically and internationally.