Associate Analytics Engineer - Hartford, CT; Hybrid

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Workplace:
Hybrid
• Remote (optional)

This position plays a role in the data ecosystem by assisting in transforming raw data into structured, high-quality datasets that are ready for analysis. The role primarily focuses on low to moderately complex business problems while receiving coaching and guidance from data leadership. The role combines the technical skills of a data engineer, the analytical mindset of a data analyst, and working business acumen to ensure data is not only collected and stored efficiently but also accessible and insightful for end users.

With some coaching and guidance from more experienced engineers, the position is responsible for end‑to‑end data workflow including data ingestion, transformation, modeling, and validation to enable data‑driven decision‑making across the organization. The position requires an understanding of data engineering, business processes, and analytics principles as well as a proactive approach to solving complex data challenges.

• Emerging data infrastructure development with coaching and guidance:
  Pipeline Design and Development – architects and builds scalable data pipelines using modern ETL (Extract, Load, Transform) tools and frameworks such as dbt (Data Build Tool), Apache Airflow, or similar. Automates data ingestion processes from various sources including databases, APIs, and third‑party services. Data Storage and Management – designs and implements data warehousing solutions using platforms like Snowflake, Redshift, or Big Query.
  
  Optimizes storage solutions for performance, cost efficiency, and scalability.
• Emerging data modeling and transformation with coaching and guidance:
  Data Modeling – develops and maintains logical and physical data models to support business analytics. Creates and manages dimensional models, star/snowflake schemas, and other data structures. Data Transformation – transforms raw data into clean, organized, and analytics‑ready datasets using SQL, Python, or other relevant languages. Implements data transformation workflows to handle data cleansing, normalization, and enrichment. Data Quality Assurance – conducts data validation and consistency checks to ensure the accuracy and reliability of data.
  
  Implements data quality monitoring and alerting mechanisms.
• Emerging collaboration and stakeholder management with coaching and guidance:
  Cross‑Functional Collaboration – works closely with data analysts, data scientists, and business stakeholders to gather requirements and understand their data needs. Acts as a liaison between technical teams and business units to translate business requirements into technical specifications. Technical Communication – clearly communicates complex technical concepts and data insights to non‑technical stakeholders. Provides training and support to team members on data tools, best practices, and methodologies.
• Emerging data governance and security knowledge with coaching and guidance:
  Governance Policies – implements and enforces data governance policies to ensure data privacy, security, and compliance with relevant regulations. Defines and manages data access, controls, permissions, and audit trails. Security Measures – monitors and enforces data security measures to protect sensitive information from unauthorized access and breaches. Ensures compliance with industry standards and regulations such as GDPR, CCPA, or HIPAA and others as applicable.
• Emerging tool and technology utilization with coaching and guidance:
  Technology Stack –…