À propos
Pay Range: $70hr - $75hr
The Senior Data & Analytics Engineer is responsible for designing and building scalable data platforms and analytics solutions to support business intelligence and marketing analytics. The role focuses on developing robust data pipelines, managing large analytical datasets, and enabling enterprise reporting through modern data architectures. This position collaborates closely with analytics, marketing, and product teams to deliver reliable data assets and high-performance reporting solutions. Requirement/Must Have:
Strong hands-on experience with Microsoft Fabric including Lakehouse, Data Factory, and Spark-based data engineering.. dvanced SQL skills with experience working on large analytical datasets Strong experience with Power BI including semantic models, DAX, and performance optimization. Proficiency in PySpark or Spark SQL. Deep understanding of data modeling concepts including star and snowflake schemas. Experience designing ETL or ELT data pipelines. Experience implementing incremental processing and change data capture strategies. Knowledge of data quality validation frameworks. Experience operating data platforms in production environments. Strong stakeholder communication skills and ability to translate business requirements into data solutions. Experience:
Extensive experience in data engineering or analytics engineering roles. Experience supporting marketing analytics, customer analytics, or growth analytics. Experience working in agile and fast-paced environments with evolving requirements. Responsibilities:
Design and implement end-to-end data pipelines using Microsoft Fabric technologies. Build and maintain Lakehouse architectures optimized for analytics and reporting. Implement incremental data loads and data freshness strategies for large-scale datasets. Optimize storage formats, partitioning strategies, and performance tuning within the data platform. Develop data transformation logic using PySpark, Spark SQL, and SQL-based transformations. Perform data cleansing, enrichment, standardization, and deduplication across multiple data sources. Implement data quality checks, validation rules, and anomaly detection within pipelines. Maintain reusable transformation frameworks and shared data assets. Ingest and model data from marketing platforms, digital analytics platforms, and internal business systems. Build conformed dimensions and fact tables for marketing performance, attribution, funnel analysis, and customer insights. Enable cross-channel reporting and identity-aware analytics. Design and optimize Power BI semantic models for enterprise reporting. Build star schemas, calculation groups, and optimized analytical measures. Ensure reporting performance, scalability, and reliable data refresh processes. Support self-service analytics while maintaining enterprise data governance standards. Collaborate with analysts and business users on dashboard requirements and usability. Implement workspace strategies, environment separation, and deployment pipelines Enforce data access controls and data security practices. Establish monitoring, logging, and alerting for pipeline health and data reliability Document data models, pipelines, and operational processes. Provide technical mentorship to engineers and analysts. Contribute to data architecture standards, naming conventions, and pipeline design practices. Should Have:
Experience with enterprise marketing technology platforms such as customer data platforms or campaign tools. Familiarity with data governance frameworks and privacy-aware data practices Experience migrating legacy business intelligence platforms to modern data platforms. Knowledge of CI/CD practices for data platforms. Experience with the broader Azure ecosystem. Skills:
Data pipeline development. Data modeling and analytics architecture. SQL and Spark-based data processing. Business intelligence and semantic modeling. Data governance and security. Performance optimization and data platform monitoring. Cross-functional collaboration and stakeholder communication. Qualification And Education:
Bachelor's degree in Computer Science, Data Engineering, Information Systems, or a related field. dvanced technical expertise in modern data platforms and analytics engineering practices.
Compétences linguistiques
- English
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