• Desarrollar pipelines de datos utilizando Databricks para procesamiento y transformación de grandes volúmenes de información. • Construir y optimizar consultas a bases de datos (SQL) asegurando eficiencia y escalabilidad. • Diseñar modelos de datos orientados a analítica y reporting. • Desarrollar dashboards y reportes en Power BI, integrando fuentes de datos diversas. • Implementar soluciones para análisis de customer journey analytics, asegurando trazabilidad y consistencia de datos a lo largo del ciclo del cliente. • Integrar múltiples fuentes de datos (internas y externas) para generar datasets confiables. • Automatizar procesos de carga, transformación y actualización de datos (ETL/ELT). • Trabajar en conjunto con equipos de negocio para traducir requerimientos en soluciones técnicas. • Asegurar buenas prácticas de desarrollo (versionamiento, documentación, testing).

Role Description You are the engine that turns complex data strategies into high-performing reality. As our Data Cloud Developer, you won’t just execute tasks; you’ll build the technical foundation that allows our clients to finally see a 360-degree view of their customers. We need your hands-on expertise to bridge the gap between architectural blueprints and live, scalable solutions that drive massive business impact. Qualifications - 5+ years of proven success in Salesforce development and implementation, with a deep technical focus on Salesforce Data Cloud. - Hands-on mastery of data modeling within the Salesforce ecosystem, including DMO/DLO mapping and identity resolution rulesets. - Technical proficiency in SQL and Data Ingestion, with experience connecting Data Cloud to external platforms like Snowflake, BigQuery, or Amazon S3. - Strong command of Salesforce automation, specifically using Flow and Apex to trigger actions based on unified profile changes. - A track record of technical problem-solving, with the ability to debug complex data streams and transformation logic independently. - Relevant certifications, such as Salesforce Data Cloud Consultant or Salesforce Strategy Designer, paired with a solid understanding of the Data Cloud lifecycle. Requirements - Build and configure end-to-end Data Cloud solutions, from initial data-stream ingestion to final activation of segments. - Transform raw data into actionable insights by writing calculated insights and mapping complex data schemas with precision. - Collaborate closely with Architects to translate high-level solution designs into functional technical requirements and clean code. - Own the technical health of the platform, ensuring data refreshes are seamless and identity resolution is performing at its peak. - Develop powerful activations that push unified data to Marketing Cloud, Sales Cloud, or external endpoints to trigger real-time customer journeys. - Stay ahead of the curve by experimenting with the latest Data Cloud releases (like Data Spaces or AI-driven features) to keep our clients at the cutting edge. Benefits - Successfully integrated into assigned client projects and are consistently hitting your billable targets. - Execute technical builds for Data Cloud implementations, ensuring 100% data accuracy and seamless system integration. - Become a "go-to" technical expert for complex Data Cloud troubleshooting, recognized for your ability to turn messy data into a unified, high-value asset.

Role Description Everforth ECS is seeking a Data Engineer to work on our US Postal Service Data Analytics team. This is an exciting position that develops, implements, and maintains data workflow and architecture solutions across large enterprise warehouses and pipelines. These solutions will be used to support effective and efficient data management and enterprise-wide business intelligence and efficiency analytics for the US Postal Service. - Implement and optimize data pipeline architectures for data sourcing, ingestion, transformation, and extraction processes, ensuring data integrity, consistency, and compliance with organizational standards. - Develop and maintain scalable database schemas, data models, and data warehouse structures; perform data mapping, schema evolution, and integration between source systems, staging areas, and data marts. - Automate data extraction workflows and develop comprehensive technical documentation for ETL/ELT procedures; collaborate with cross-functional teams to translate business requirements into technical specifications and data schemas. - Establish and enforce data governance standards, including data quality metrics, validation rules, and best practices for data warehouse design, architecture, and tooling. - Develop, test, and deploy ETL/ELT scripts and programs using SQL, Python, Spark, or other relevant languages; optimize code for performance, scalability, and resource utilization. - Implement and tune data warehouse systems, focusing on query performance, batch processing efficiency, and resource management; utilize indexing, partitioning, and caching strategies. - Perform advanced data analysis, validation, and profiling using SQL and scripting languages; develop data models, dashboards, and reports in collaboration with stakeholders. - Conduct testing and validation of ETL workflows to ensure data loads meet scheduled SLAs and business quality standards; document testing protocols, results, and remediation steps. - Perform root cause analysis for data processing failures, troubleshoot production issues, and implement corrective actions; validate data accuracy and consistency across systems; support iterative development and continuous improvement of data pipelines. Qualifications - Must be a US Citizen or Green Card holder, and must be able to obtain a Public Trust Clearance. - 5-10+ years of experience building and designing data extraction, formatting and engineering tools, workflows, pipelines and ETL / ELT processes. - Detail oriented with strong analytical and problem-solving skills. - Ability to use database tools, techniques, and applications (e.g., Teradata, Oracle, Non-Relational) to develop complex SQL statements (e.g., multi-join), and to tune and troubleshoot queries for optimal performance. - Skill using Unix/Linux shell scripting to develop and implement automation scripts for Extract, Transfer Load (ETL) processes. - Communications skills (both verbal & written) - ability to work and communicate with all levels in team structure. - Team player with the ability to prioritize and multi-task, work in a fast-paced environment, and effectively manage time. - Java/J2EE and REST APIs, Web Services and building event-driven Micro Services and Kafka streaming using Schema registry, OAuth authentication. - Spring Framework and GCP Services in public cloud infrastructure, Git, CI/CD pipeline and containerization, data ingestion/data modeling. - Develop Microservices using Java/J2EE Spring for ingesting large volume real-time events into Kafka topics. Architect solutions that make the data available to consumers in real time. Requirements - Salary Range: $130,000-$145,000 Benefits - General Description of Benefits

• Design and implement scalable data models and pipelines on Snowflake using Snowpark and SQL • Write complex, production-grade SQL (dbt models, views, UDFs) to power business logic and product features • Build data-facing application layers using Streamlit (in Snowflake) or similar frameworks • Develop and iterate on greenfield product features across AI Pod and Rights360 platforms • Create user-facing applications on Snowflake, with potential extension into lightweight APIs (e.g. FastAPI) • Leverage Snowflake Cortex AI/ML services for forecasting, profitability modelling, and conversational/agentic features • Contribute to real-time inventory management and rights conflict detection (Rights360) • Build conversational interfaces enabling natural language data querying for operational teams • Collaborate closely with the shared tech lead across multiple workstreams • Take end-to-end ownership of solutions — from data modelling and logic to UI delivery • Ensure production-ready systems with a focus on reliability, maintainability, and clean code • Work across the full stack, from data pipelines to user-facing dashboards