Strategic Imperative:
The
Analytics Engineer I
plays a key role in supporting the organization’s business intelligence and reporting infrastructure by transforming raw data into trusted, actionable insights. This individual will focus on building curated data models, developing dashboards, and enabling accurate and timely reporting for business stakeholders. They will work under the guidance of experienced Analytics Engineers to design semantic layers, validate data logic, and ensure analytics outputs are aligned with business needs. This role offers a strong foundation in modern analytics engineering practices, including hands-on experience with tools like Snowflake Data Cloud, dbt, Sigma Computing, and our Iceberg-based data lake, while also supporting foundational AI and ML initiatives by preparing high-quality, model-ready datasets and enabling applications such as retrieval-augmented generation (RAG) and agent-based workflows.
Prodege
A cutting-edge marketing and consumer insights platform, Prodege has charted a course of innovation in the evolving technology landscape by helping leading brands, marketers, and agencies uncover the answers to their business questions, acquire new customers, increase revenue, and drive brand loyalty & product adoption. Bolstered by a major investment by Great Hill Partners in Q4 2021 and strategic acquisitions of Pollfish, BitBurst & AdGate Media in 2022, Prodege looks forward to more growth and innovation to empower our partners to gather meaningful, rich insights and better market to their target audiences.As an organization, we go the extra mile to “Create Rewarding Moments” every day for our partners, consumers, and team. Come join us today!
Primary Objectives
- Translate business needs into trusted data models and reporting logic that support decision-making and business transparency
- Design and build dashboards and visualizations that clearly communicate key metrics and performance trends
- Develop and maintain curated dbt models and semantic layers that power reporting, ad hoc analysis, and AI/ML applications
- Contribute to data governance by validating definitions, ensuring consistency, and acting as a data steward for assigned domains
Qualifications-
To perform this job successfully, an individual must be able to perform each job duty satisfactorily. The requirements listed below are representative of the knowledge, skill, and/or ability required. Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions.
Detailed Job Duties:(typical monthly, weekly, daily tasks which support the primary objectives)
- Create and maintain dashboards and reports that effectively communicate key business metrics and trends
- Collaborate with stakeholders to gather requirements and translate business needs into analytic data models and reporting logic
- Build and maintain curated data models and semantic layers (e.g., dbt models) used in reporting, analysis, and decision-making
- Support AI/ML initiatives by preparing model-ready datasets and enabling use cases like retrieval-augmented generation (RAG) and agent-based applications
- Perform exploratory and ad hoc analysis to support decision-making and uncover insights
- Champion data quality and consistency by validating logic, reviewing definitions, and supporting governance initiatives
What Success Looks Like
Success in the Analytics Engineer I role entails becoming a trusted data partner, empowering the organization with insightful visualizations and ensuring data reliability. You will bridge the gap between business needs and data insights, fostering a data-driven culture through effective collaboration and clear communication. By maintaining curated data models, high-quality reporting assets, and championing data quality, you'll ensure the integrity of our data ecosystem. You'll also play a key role in enabling AI and ML initiatives by delivering model-ready data and supporting intelligent applications, while continuously growing your skills as a valuable data expert and contributing to the achievement of our strategic objectives.
The MUST Haves:(ex: skills, education, experience, certifications, licenses)
- One or more (1+) years of experience working with data in a technical or analytical capacity.
- Demonstrated exposure to AI or ML concepts through coursework, certifications, personal projects, or work experience (e.g., preparing data for ML models or using AI-assisted analytics tools).
- Proficient in SQL, with the ability to write complex queries involving joins, filtering, and aggregations.
- Solid understanding of relational databases, including concepts like schemas, tables, keys, and normalization and the differences between OLTP and OLAP systems.
- Strong analytical and problem-solving skills, with the ability to break down complex BI tasks and deliver actionable solutions with measurable results.
- Excellent communication and collaboration abilities, with a strong inclination toward cross-functional teamwork and knowledge sharing.
The NICE To Haves
- Bachelor’s degree in Computer Science, Information Systems, Data Science, or related field; master’s degree or industry-specific certifications preferred.
- Experience with Snowflake, Sigma Computing, dbt, Airflow or python
- Exposure to AI applications such as building basic agents, implementing retrieval-augmented generation (RAG), or supporting data pipelines that enable machine learning or large language model use cases.
