FBS Analytics Engineer

Job Title

This position plays a crucial role in the data ecosystem by iteratively transforming raw data into structured, high-quality datasets that are ready for analysis in partnership with data/decision scientists.

The role primarily focuses on moderately complex business problems while receiving limited coaching and guidance from data leadership. The role combines the technical skills of a data engineer, the analytical mindset of a data analyst, and strong business acumen to ensure data is not only collected and stored efficiently but also made accessible and insightful for end users. In partnership with data/decision scientists, 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. This position requires deep understanding of data engineering, business processes, and analytics principles as well as a proactive approach to solving complex data challenges.

Essential Job Functions:

• Data infrastructure development: 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 BigQuery. Optimizes storage solutions for performance, cost efficiency, and scalability.
• Data modeling and transformation: 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.
• Collaboration and stakeholder management: 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.
• Data governance and security knowledge: 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.