• Design, develop, test, document, deliver, and monitor our modern Java web applications. • Ensure code quality and consistency through code reviews and system design analysis. • Identify opportunities to improve development methodologies and fine-tune internal processes.

• Implementing backend services for key CloudTalk features with a main focus on billing • Working hands-on with Node.js, TypeScript, PHP, MySQL/MariaDB, Docker, Kubernetes, Jira, and Git • Tackling tough technical challenges • Designing and implementing new features • Joining in product and architecture discussions • Collaborating with a diverse, international team

• Independently own and deliver significant features from technical design to execution and launch, effectively navigating moderate ambiguity. • Write well-crafted, highly performant, well-tested, and maintainable code for large-scale distributed systems. • Actively contribute to technical specs and system architecture discussions, collaborating closely with senior engineers to design scalable, fault-tolerant cloud solutions. • Raise the engineering bar by conducting rigorous code reviews, sharing knowledge through tech talks or brown bags, and promoting engineering best practices. • Take a proactive role in system health. Drive root cause analysis for complex bugs, resolve technical debt, and identify system improvements during on-call rotations and post-mortems. • Partner effectively with internal stakeholders (Product, TPMs, Data Science) to gather requirements, communicate technical trade-offs, and unblock project delivery.

• Data Cleaning, Parsing & Standardization (Core Function) • Build Python (Pandas/Regex) workflows to extract structure from free-form Prisma text fields containing placements, publishers, formats, devices, tactics, etc. • Develop rule-based and pattern-based logic to recognize abbreviation variations, naming inconsistencies, and agency-specific formatting habits. • Create normalization scripts that standardize publisher names, platform types, channels, and other key taxonomies. • Maintain “classification dictionaries” and pattern libraries that improve accuracy over time. • Data Quality & Operational Support • Profile incoming agency data to identify common quality issues and propose concrete remediation. • Design QA checks to detect missing fields, malformed entries, inconsistent dimensional labels, and mismatched spend totals. • Partner closely with Data Engineering to embed your logic into scheduled pipelines. • Own recurring operational workflows that support data ingestion, aggregation, and quality monitoring. • Dimensional Enrichment, Modeling & Data Derivation • Build and maintain predictive models to support media spend forecasting, pricing analysis, and text classification for data tagging and enrichment. • Apply statistical modeling or regression techniques to impute missing values or infer incomplete spend or performance metrics. • Derive new fields—such as format groupings, platform classifications, creative types, and targeting attributes—using pattern recognition and model-based inference across text fields. • Produce structured dimensions from unstructured data to improve downstream reporting. • Document the logic and assumptions behind model-driven derivations to ensure transparency and reproducibility. • Analysis & Internal Stakeholder Support • Investigate anomalies, unexpected trends, and data gaps by tracing issues back to source fields. • Build dashboards that help internal teams observe data quality, volume, deviations, and model-driven insights. • Provide ad-hoc deep dives into digital spend patterns, forecast accuracy, and classification performance for product, ops, and leadership.