Data Engineer

• Design, build, and maintain end-to-end data pipelines for media, reputation, and stakeholder datasets, from ingestion and scraping through preprocessing, normalization, and storage
• Implement and enforce data hygiene standards so analytical datasets are cleanly integrated, well-documented, and easily accessible across the Insights & Analytics team
• Operationalize and productionize advanced analytics workflows in Python and SQL including feature engineering, model scoring, and metric computation
• Develop and maintain derived metrics and indices that can be reused across clients and products
• Architect and monitor analytics systems ensuring accuracy, reliability, and performance at scale
• Integrate third-party analytics tools and media data sources (e.g., social media APIs, alternative datasets) into the PublicRelay analytics stack
• Implement rigorous logging, monitoring, and alerting for data pipelines and analytics services to catch issues early and minimize downtime
• Apply core statistics and data science methods (regression, classification, clustering, time-series analysis, sampling, A/B testing) to support new metrics, models, and analytics features
• Build and maintain ML workflows (training, evaluation, deployment) for tasks such as NLP, sentiment analysis, topic modeling, classification, and entity-level analytics
• Design and implement AI- and LLM-powered agents to automate repetitive analytics tasks, data enrichment, tagging, and insight surfacing across large-scale media datasets
• Experiment with agentic workflows (e.g., orchestrating multi-step pipelines, tool-using agents, retrieval-augmented systems) to increase speed, reliability, and sophistication of analytics outputs
• Collaborate with data scientists and insights strategists to translate experimental models and prototypes into robust, production-grade systems
• Build and optimize Tableau-ready data models that power client-facing dashboards and internal analytics tools
• Ensure datasets are structured, documented, and performant for self-serve analysis in Tableau and SQL by non-engineering stakeholders
• Partner with Visualization and Reporting teams to maintain consistent data definitions, metric logic, and calculation standards across dashboards and reports
• Contribute to internal templates and component libraries (data sources, calculated fields, parameter patterns) that speed up dashboard development and maintain consistency
• Master PublicRelay’s proprietary platforms and data schemas to design systems that fit seamlessly into existing workflows
• Partner closely with Insights, Reporting, Engineering, and Client Success teams to understand how they use analytics and translate those needs into scalable data solutions
• Participate in design reviews and technical scoping for new analytics capabilities, providing recommendations on architecture, data models, and feasibility
• Act as a go-to technical partner for Insights teams during experiments and pilots, helping them test new metrics, methodologies, and frameworks rapidly and safely
• Deliver all projects within agreed timelines while maintaining high standards for code quality, testing, and documentation
• Conduct regular QA on source data, transformations, and metrics to ensure accuracy, completeness, and consistency across systems
• Proactively identify technical and process bottlenecks
• propose and implement improvements that increase the speed, reliability, and scalability of analytics delivery
• Communicate status, risks, and tradeoffs clearly to technical and non-technical stakeholders
• flag issues early with proposed options

• Has deep expertise in data scraping, ingestion, preprocessing, normalization, and data management best practices in a production environment
• Demonstrates strong command of Python and SQL, with experience building and maintaining data pipelines and analytics services
• Applies statistical and data science methods confidently (e.g., regression, classification, clustering, time-series, sampling, hypothesis testing)
• Has hands-on experience with ML and NLP in real-world settings (e.g., classification, sentiment analysis, topic modeling, entity extraction, summarization)
• Is fluent with AI/LLM and agentic tools (e.g., using APIs, orchestration frameworks, or workflow engines) and is eager to experiment with new approaches
• Is comfortable building Tableau-ready data models and collaborating with dashboard developers to ensure performance and usability
• Is an innovative and creative thinker who enjoys connecting disparate data sources and systems into cohesive analytics solutions
• Has a strong ability to explain technical concepts to a non-technical audience
• Thrives in collaborative environments and partners effectively with insights professionals, analysts, and client-facing teams
• Takes an ownership mindset, holds a high bar for quality, and is motivated by building systems that others can rely on
• 5-10 years of experience in data engineering, analytics engineering, or applied data science roles, ideally in analytics-heavy, product, or consulting environments
• Advanced proficiency in Python (pandas, NumPy, SQLAlchemy or similar) and SQL (data modeling, performance optimization, complex queries)
• Experience with modern data stack components (e.g., workflow/orchestration tools, cloud data warehouses, version control, CI/CD) in production settings
• Practical experience with ML and statistics in Python (e.g., scikit-learn, statsmodels, NLP libraries) and deploying models into production workflows
• Experience preparing data for Tableau or similar BI tools
• understanding of best practices for semantic layers, extracts, and performance tuning
• Strong understanding of data architecture fundamentals, including schema design, ETL/ELT patterns, and data quality frameworks
• Excellent communication skills with the ability to explain complex technical concepts clearly to non-technical partners
• Bachelor’s degree in Computer Science, Engineering, Statistics, Data Science, or a related field
• advanced degree is a plus

advanced degree is a plus