Top Data & Analytics Jobs in Hartford, CT

Own the end-to-end architecture and roadmap for MyFitnessPal’s product data stack. Coordinate data engineering, business intelligence, product analytics, and backend teams; establish data movement and standardization practices; lead tool evaluations and platform migrations; implement governance, PII handling, deletion workflows, access controls, and cost optimization; and document architecture standards across the organization.

Design, develop, and maintain Customer Data Platform solutions and data pipelines to create unified customer profiles, manage identity resolution, enable audience segmentation and activation, ensure data quality and governance, and support integrations with marketing, CRM, and cloud data systems.

Executes eDiscovery analytics and AI-assisted workflows in Relativity and related platforms, including TAR, continuous active learning, generative AI classifiers, validation sampling, statistics, and structured analytics. Maintains accurate matter records, tracking, time entries, audit trails, and quality controls. Provides weekend coverage, monitors request queues, supports deadline-driven projects, documents handoffs, and escalates sensitive issues. The role includes structured training and progression toward an eDiscovery consultant track.

Own executive, board, investor, financial, and operational reporting for leadership. Build scalable dashboards, define consistent KPIs, reconcile data across systems, investigate performance changes, and translate complex business questions into actionable insights. Partner with Strategy, Finance, and senior leaders to automate recurring reporting, improve data accuracy, and support actuals-versus-budget-versus-forecast analysis.

Owns analytics for BILL’s payments products, setting the measurement agenda, defining KPIs and OKRs, building production-grade dbt models, and conducting experimentation and causal inference. Partners with Product, Engineering, Finance, Marketing, and leadership to identify opportunities, improve data quality, automate reporting, and influence roadmap decisions. Presents executive insights and defends metric definitions and recommendations.

Processes client-requested data changes, creates data transfer packages, and writes SQL queries against Microsoft SQL databases. Conducts risk assessments, verifies data updates and transfer datasets, creates stored procedures, monitors data across databases, and supports clinical study implementation. Requires strong attention to detail, documentation, communication, prioritization, and occasional after-hours technical support.

Build customer-facing data products for the single-family rental market. Responsibilities include architecting real estate data models, designing reliable data pipelines and workflows, shaping data platform architecture, establishing engineering standards, and developing real estate domain expertise. The role partners with data science and product teams and requires leadership in scalable data engineering.

Designs and implements scalable data pipelines, cloud data platforms, storage solutions, and analytics infrastructure. The role focuses on Snowflake and AWS architecture, migrating legacy systems to cloud environments, developing Python and SQL data-processing logic, orchestrating workflows, and implementing Infrastructure-as-Code and CI/CD. The engineer also contributes to data governance, quality, observability, stakeholder communication, mentoring, and technical leadership.

The Senior Data Analyst will develop and improve Sigma dashboards, reporting, and business intelligence capabilities. Responsibilities include gathering stakeholder requirements, querying and analyzing complex datasets with SQL, improving data quality and governance, defining metrics, automating reporting processes, and partnering on Snowflake data modeling and warehouse improvements. The role also evaluates analytics tools, delivers actionable insights, and communicates findings to technical and non-technical audiences.

Monitors, validates, and improves data quality across device, program, operational, and field sources. Designs automated checks, reconciles discrepancies, investigates root causes, documents findings, and communicates impacts and recommendations. Collaborates with analytics, engineering, MEAL, and country teams to resolve issues. Maintains reproducible analyses using SQL, programming, version control, and notebooks while supporting data governance, reporting standards, and responsible handling of sensitive information.