Data Engineer

About this role

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Skills you'll use

What you'll do

• 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.

Who can apply

• 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.

Perks & benefits

Interview & selection process

3 rounds — typical selection flow for this role:

1. 1Apply — submit your profile through SRKay's online portal
2. 2Screen — initial review and comprehensive phone screening
3. 3Interview — technical assessments and cultural-fit interviews
4. 4Offer

About SRKay Consulting Group

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