Data Engineer

Job Overview

The Data Engineer is responsible for building and maintaining the technical backbone that powers PublicRelay’s analytics, measurement, and narrative intelligence products. You will design and operate robust data pipelines, analytical systems, and derived metrics that enable our Insights & Analytics teams to deliver sophisticated, business-aligned analytics at scale for global communications and reputation leaders. You will work at the intersection of data engineering, applied data science, and AI/agentic tooling, with a mandate to ensure our data is clean, reliable, and production-ready while continually pushing the frontier of what our analytics stack can do. This role is approximately 70% engineering and systems development and 30% applied analytics and experimentation in partnership with Insights teams.

Key Responsibilities

• 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.

Requirements & Skills

• 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.