Principal Data Engineer

Das ist der Job

This role is not only about building pipelines.

Darum lohnt es sich

Job Description Strategic Imperative The Principal Data Engineer role is integral to the success of Prodege’s core business by serving as a highly technical, hands‑on leader responsible for designing, building, and modernizing the next‑generation data platform.

It is about shaping the long‑term data architecture, guiding tool and design decisions, mentoring the team, partnering across ML, BI, Product, and Engineering, and helping the organization adopt a more AI‑first approach to solving complex data engineering problems.

As an organization, we go the extra mile to “Create Rewarding Moments” every day for our partners, consumers, and team.

Primary Objectives Architecture & Modernization: Lead the design and implementation of the next-generation Lakehouse architecture (Iceberg / Trino / Snowflake) and refactor complex legacy data systems into modern, scalable patterns.

High-Performance Pipeline Delivery: Design, build, and optimize high-scale, reliable ELT / ETL and streaming data pipelines using expert-level SQL, Python, Snowflake, dbt, and modern orchestration patterns.

AI / ML Enablement: Directly support Data Science and ML Engineering teams by delivering production‑grade datasets, feature pipelines, and scalable data foundations for experimentation, model development, and decisioning.

Engineering Excellence & Mentorship: Elevate the engineering bar across the team, set architectural standards, mentor engineers, and champion AI‑assisted / AI‑first development practices where they meaningfully improve productivity and quality.

Architecture & Modernization Architect, design, and implement components of the next-generation data platform / Lakehouse, leveraging Iceberg, Trino, Snowflake, and related modern data technologies.

Lead the simplification and refactoring of complex, high‑volume legacy pipelines, migrating them toward modern, declarative ELT patterns (primarily via dbt) and scalable streaming / event-driven designs where appropriate.

High-Performance Pipeline Delivery Design, build, and maintain scalable, reliable data pipelines (batch and near‑real‑time) using Python, expert‑level SQL, orchestration tools (e.g., Airflow or similar), and modern data platform components.

Collaborate with Data Governance and Security teams to enforce data access controls, PII handling, retention policies, and compliance requirements. AI / ML Enablement Work closely with Data Science and ML Engineering teams to understand and enable their training, inference, experimentation, and data serving needs.

Set high technical standards for code quality, testing, documentation, reliability, and maintainability within the Data Engineering team. Help grow the team’s capability to solve increasingly complex data problems through the right combination of tooling, process, architecture, and talent.

Guide teams on the selection and use of the right data tools, technologies, and platform components across the data lifecycle. Proven experience designing and building modern data platforms at scale.

Strong understanding of Medallion architecture, modern data modeling techniques, data contracts, schema evolution, and platform design patterns for analytics and ML. Experience partnering cross-functionally with Data Science, BI, Product, Engineering, and business teams to build scalable and trusted data foundations.

Ability to bring an AI-first mindset to the data engineering organization and help teams use AI effectively to solve complex data engineering problems. Strong mentoring and technical leadership skills with ability to influence architecture and engineering direction across teams.

Please note that the compensation details listed in US role postings reflect the base salary only, and do not include bonus, equity, or benefits. Prodege Benefits Prodege offers a comprehensive benefits package to US Full‑time employees including medical, dental, vision, STD, LTD and basic life insurance.

This engineer will be at the center of transforming our data architecture across Snowflake, dbt, Iceberg, Trino, and streaming/event‑driven systems, ensuring the data foundation is scalable, reliable, cost‑efficient, and AI‑ready to power our flagship products (Swagbucks, MyPoints, Insights) and complex business domains (CX, Rewards, Performance Marketing).

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 Blackstone in Q1 2026, Prodege looks forward to more growth and innovation to empower our partners to gather meaningful, rich insights and better market to their target audiences. Come join us today!

Data Quality & Governance: Own the observability, lineage, quality, reliability, and governance frameworks for mission‑critical datasets across the multi‑product ecosystem.

Strategic Data Leadership: Guide the organization on Medallion architecture, data contracts, schema evolution, tool selection, and scalable platform patterns while operating effectively in ambiguous and evolving problem spaces. 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.

Define and implement best practices for data storage, partitioning, clustering, schema evolution, and query design to optimize performance, reliability, and cloud compute cost. Define and evangelize scalable architectural patterns, including Medallion architecture, data contracts, schema management, and platform standards across the data lifecycle.

Help the organization make pragmatic architectural decisions in ambiguous environments, balancing long-term platform design with short-term business needs. Develop and enhance Snowflake data models, dbt models, and high-performance analytical data marts for consumption by BI, reporting, product applications, experimentation, and ML systems.

Own the entire pipeline lifecycle: requirements gathering → design → build → testing → deployment → monitoring → iteration. Design and guide streaming and event-driven architectures where needed to support real‑time or near‑real‑time use cases.

Data Quality & Governance Implement and enhance data lineage, quality checks, observability, alerting, and reliability practices across core data pipelines and datasets. Continuously monitor and tune pipeline performance to meet strict data SLAs / SLOs.

Establish high standards for trustworthy, well-governed data that can serve as the foundation for BI, ML, and business decision-making. Design and optimize data feeds for high-volume ML workloads, including feature pipelines, training datasets, and feature-store‑like patterns where needed.

Ensure data consistency, quality, and integrity for critical AI-driven applications across consumer and business products. Help build the data platform in a way that supports experimentation, model iteration, and scalable ML use cases across Performance Marketing, Rewards, CX, and related domains.

Engineering Excellence & Mentorship Actively use AI-assisted development tools (e.g., Copilot, Claude, Gemini, or similar) to accelerate coding, testing, documentation, troubleshooting, and architectural exploration.

Drive a strong AI-first mindset within the data organization by identifying where AI can improve developer productivity, pipeline development, debugging, design exploration, and documentation — while maintaining strong validation discipline.

Provide technical leadership and mentorship to junior and mid-level engineers, running design reviews and driving consensus on architectural trade-offs. Cross-Functional Collaboration Partner closely with ML, BI, Product, Engineering, Analytics, and business stakeholders to ensure the data platform supports real business and product needs.

Translate business needs and ambiguous requirements into scalable, practical technical designs. The MUST Haves Ex: job cannot be done without these skills/competencies, education, experience, certifications, licenses. Bachelor’s degree in Computer Science, Engineering, a quantitative field, or equivalent practical experience.

Six or more (6+) years of hands‑on experience in Data Engineering, ideally in AdTech, MarTech, Growth, consumer internet, or other high-volume / multi‑product environments. Expert-level proficiency in SQL, strong Python, and extensive experience building robust ETL / ELT workflows.

Strong experience with Snowflake and dbt for data transformation and analytics engineering. Strong experience with batch and near‑real‑time data pipelines, event‑driven systems, and/or streaming architectures. Proven experience with performance tuning of large queries, cost / performance tradeoffs, and reliability of data infrastructure.

Experience with Iceberg, Trino, or similar open table format / query engine ecosystems in a Lakehouse architecture. Ability to navigate and refactor complex, interconnected data systems with an ownership mindset (“you build it, you run it”). Comfortable working through ambiguity and helping define strategy in greenfield or evolving environments.

The Nice to Haves Experience with Kafka, Kinesis, or Apache Flink for streaming ingestion and event-driven data architectures. Familiarity with feature stores, model-serving pipelines, and MLOps practices. Professional experience using AI-driven development tools (e.g., GitHub Copilot, etc.) for coding, testing, or documentation generation.

Prior experience in a consumer rewards, survey, or performance marketing ecosystem. Pay Transparency The anticipated base salary range for this position is $240,000 to $275,000.

The final salary offered to a successful candidate will be dependent on several factors that may include, but are not limited to; the type and length of experience within the job, type and length of experience within the industry, the type and length of knowledge and skills for the position, education, training, etc.

Prodege is a multi-state employer and final compensation within this range could be impacted by work location. Employees receive flexible PTO, as well as paid sick leave prorated based on hire date. US Employees have eight paid holidays throughout the calendar year.

Employees receive an option to purchase shares of Company stock commensurate with their position, which vests over four years. Equal Employment Opportunity Statement At Prodege, we are committed to creating a diverse and inclusive environment.

We are proud to be an Equal Opportunity Employer We do not discriminate on the basis of race, color, religion, sex, sexual orientation, gender identity or expression, national origin, age, disability, veteran status, or any other characteristic protected by law. We encourage individuals of all backgrounds to apply.

FCIHO Employers will consider for employment qualified applicants with criminal histories in a manner consistent with the requirements of FCIHO. #J-18808-Ljbffr