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Own and rationalize a legacy Finance reporting inventory for a modern data platform. Responsibilities include prioritizing delivery backlogs, defining acceptance criteria, facilitating requirements validation, resolving stakeholder ambiguity, coordinating with architecture, security, and development teams, and tracking progress through design, build, and cutover milestones.

Design, develop, and optimize scalable data pipelines using Databricks, Apache Spark, SQL, Python, Scala, and Azure. Process large datasets, improve pipeline performance and reliability, implement data quality and governance practices, troubleshoot complex issues, document solutions, and collaborate with technical teams, data professionals, business stakeholders, and clients.

Develop and execute manual and automated tests for web and mobile software, including API testing. Create test plans, identify and document defects, collaborate with developers on fixes, validate customer requirements, and provide feedback to improve product and process quality.

Performs data quality checks, financial reconciliations, and legacy-to-modernized report validation. Investigates data issues, documents root causes, builds Power BI quality dashboards, and partners with Finance and business stakeholders to establish acceptance criteria and thresholds. Produces reconciliation evidence and sign-off packages for delivery and cutover gates, while documenting rules and known issues for client handoff.

Develop and maintain scalable data pipelines, ETL/ELT processes, data architectures, warehouses, and integrations. Ensure data quality, governance, security, performance, and reliability while collaborating with analytics, data science, and business teams. The role also requires documenting solutions, optimizing cloud data platforms, and using AI tools to improve engineering workflows.

Own the operational foundation of a production cloud data platform, including environment provisioning, CI/CD pipelines, automated regression testing, monitoring, alerting, cost attribution, and reliability governance. Develop runbooks, maintain architecture artifacts, and lead progressive knowledge transfer to the client’s operations team. The role supports lakehouse, cloud warehouse, CDC replication, and finance data platform modernization.

Designs and implements scalable data pipelines, cloud data infrastructure, dimensional models, and Data Lake/Data Warehouse solutions. Maintains data quality, governance, cataloging, lineage, and ETL CI/CD processes. Requires strong data engineering experience with Azure, SQL, Spark, Azure Data Factory, Azure DevOps, and modern data stacks, plus advanced English and international client experience.

Build and maintain curated data layers for a large-scale finance data platform using versioned SQL transformations, dimensional modeling, reconciliation, and data-quality checks. Recompute business logic, conform source structures, and enable BI consumers through governed data objects. Participate in code reviews, CI/CD promotion, and automated regression testing while collaborating with distributed engineering teams and financial-services stakeholders.

Design and implement scalable data pipelines, cloud data infrastructure, dimensional models, and Data Lake/Data Warehouse solutions. Ensure data quality, governance, cataloging, lineage, and accessibility while collaborating with international clients and cross-functional teams. Configure CI/CD pipelines for ETL processes using modern data stacks, Azure services, SQL, Spark, Python, Snowflake, dbt, Airflow, and Dataform.

Lead the lifecycle of data products from ideation through optimization. Analyze data to support business and digital product growth, lead cross-functional teams of data scientists and engineers, prioritize initiatives, define roadmaps, communicate value to stakeholders, and develop data talent. The role requires strategic thinking, product management skills, customer segmentation expertise, Power BI proficiency, international client experience, and fluency in English and Spanish.