• Develop and maintain ETL/ELT data pipelines in high-volume, complex environments using Apache Spark (Scala and Python) and AWS; • Contribute to the evolution and expansion of the Analytics Platform, focusing on Big Data architectures and technologies; • Gather, analyze, and translate business requirements into technical data solutions; • Design and maintain analytical data models, metrics, and KPIs; • Integrate multiple data sources, including streams (Apache Kafka / MSK) and SQL and NoSQL databases; • Develop, operate, and evolve data pipelines in production environments; • Ensure scalability, performance, reliability, data quality, and governance; • Prepare, cleanse, and make data available for analytical and operational consumption; • Automate the creation, configuration, and monitoring of data pipelines and environments; • Manage SLAs, handle failure remediation, and resolve incidents in data flows; • Work collaboratively in agile squads, supporting technical decisions and promoting data engineering best practices; • Provide technical mentorship and share knowledge within the team.

• Maintain a full portfolio of new CRM clients, serving as their data point of contact throughout implementation. • Conduct data imports by preparing complex data import rules for software engineers. • Perform data validation checks throughout the process to ensure data integrity and accuracy, including pre-migration audits and post-migration reconciliation. • Examine source Excel data to identify its structure, quality, and potential issues like inconsistencies, missing values, or non-standard entries. • Document the entire migration process, including data maps, transformation logic, and any issues encountered, and communicate with business stakeholders and development teams to ensure all requirements are met. • Address and resolve any issues that arise during or after the migration, such as data import errors or data discrepancies. • Manage all projects efficiently by keeping relevant internal systems accurate and up to date, including recording progress in ChurnZero, ZenDesk, JIRA, Salesforce, and other company systems. • Collaborate effectively with internal cross-functional teams (e.g., Sales, Implementation, Support) to ensure alignment on client strategy, project status, and a cohesive, unified service experience throughout the migration lifecycle.

Role Description We are currently searching for a Solution Data Engineer Sr : - Full management of the Databricks environment, including configuration and workspace administration. - Implement data governance using Unity Catalog and manage structured storage with Delta Lake tables. - Write and optimize data processing logic using Python, SQL, and PySpark. - Manage data architecture and performance within Snowflake. - Handle Adobe Data Feeds and Adobe Analytics, ensuring correct ingestion and transformation of marketing data. - Oversee data warehousing operations in Amazon Redshift. - Schedule and monitor complex data workflows using Airflow. - Own end-to-end maintenance of data pipelines, ensuring reliability from ingestion to delivery across multiple cloud platforms. - Manage code lifecycle and collaborative development using Git. Qualifications - Advanced proficiency in the Databricks Ecosystem (Workspace admin, Delta Lake, Unity Catalog). - Expertise in Data Warehousing & Integration, specifically with Snowflake. - Strong development skills in Python, SQL, and PySpark. - Hands-on experience with Adobe Data Ingestion (Data Feeds/Analytics). - Solid experience in version control using Git. Requirements - Experience with Amazon Redshift for data warehousing operations. - Proficiency in orchestration tools, specifically Airflow. - Familiarity with Google Cloud Platform (BigQuery) operations. - Knowledge of AWS Data Services such as Athena and AWS Glue. Benefits - Fully remote position.

• Build and maintain data ingestion pipelines using Azure Data Factory (ADF) to move data into the corporate Azure Data Lake and related platforms. • Develop and enhance data transformation logic in Databricks notebooks, primarily leveraging SQL-based transformations in existing SQL scripts and stored procedures. • Support a medallion data architecture (Bronze, Silver, Gold) with Silver and Gold datasets stored primarily in Parquet format. • Assist in migrating SQL Server–based processes (scripts, stored procedures, SSIS logic) running on Azure VMs into Azure-native pipelines and Databricks transformations. • Continuously update and enhance existing database solutions and systems based on new and evolving requirements from marketing campaign activation and analytics teams, ensuring data remains accurate, timely, and fit for purpose. • Manage multiple concurrent initiatives, including modernization efforts and ongoing enhancement requests, while maintaining platform stability and data quality. • Configure Azure Data Factory pipelines to orchestrate Databricks notebooks and Fabric-related processes as applicable. • Use Azure DevOps for source control, versioning, and deployment of Azure Data Factory, Databricks, and Fabric assets. • Test, monitor, and troubleshoot pipelines to ensure data reliability, performance, and quality. • Collaborate with senior engineers, project managers, campaign services, analytics, and platform teams to deliver ongoing data solutions. Participate in the adoption and use of Microsoft Fabric, understanding how Fabric components (e.g., OneLake, Lakehouse, Warehousing) integrate with existing Azure and Databricks workflows. • Maintain technical documentation for data pipelines, transformations, and datasets