Sr. Advanced Marketing Analytics Analyst
New
Based in the United States, PST or MSTContractSenior
Salary85 USD per hour
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Job Details
- Required Skills
- PythonSQLSnowflakeTableauData engineeringSparkScalaDatabricks
Requirements
- Proven experience designing, building, and maintaining scalable data pipelines using Databricks, Spark, Scala, Python, SQL, and Snowflake.
- Strong SQL expertise, including data extraction, transformation, optimization, and performance tuning.
- Experience performing source-to-target data reconciliation, auditing, validation, and anomaly detection across large datasets.
- Strong understanding of data quality frameworks, validation methodologies, and data governance principles.
- Hands-on experience troubleshooting and optimizing Spark-based workloads and performance issues.
- Expertise in Snowflake architecture, data loading processes, and update strategies.
- Advanced Tableau dashboard development skills, including performance optimization, parameter configuration, and data visualization best practices.
- Ability to document technical workflows, data lineage, business requirements, and pipeline architectures effectively.
- Strong analytical thinking and problem-solving capabilities with a keen attention to detail.
- Excellent communication skills and the ability to present technical concepts and findings to non-technical stakeholders.
Responsibilities
- Design, develop, and maintain scalable, reliable, and auditable data pipelines using Databricks, Spark, Scala, Python, SQL, and Snowflake.
- Manage the end-to-end data validation lifecycle, including source-to-target reconciliation, data profiling, auditing, and anomaly detection activities.
- Conduct advanced data mining and exploratory analysis to identify trends, inconsistencies, risks, and opportunities for business improvement.
- Develop and maintain Tableau dashboards and visualizations that provide transparency and support data-driven decision-making.
- Implement automated data quality monitoring, validation checks, and alerting mechanisms to identify pipeline failures and data anomalies.
- Collaborate with analysts, data scientists, and business stakeholders to gather requirements and deliver high-quality data solutions.
- Optimize existing data pipelines for scalability, performance, efficiency, and operational reliability.
- Maintain clear documentation of data lineage, metadata, transformation logic, and workflow processes.
- Support ongoing improvements to data governance, reporting accuracy, and analytics infrastructure.
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