Data Analytics
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Responsibilities:
Model and transform data into consistent analytical structures. Build and optimize ELT pipelines and quality tests. Ensure clear documentation (business rules, lineage, transformation logic). Monitor datasets, quality, SLAs, and performance. Support DS/BI/ML with reliable data and semantic layers. Collaborate closely with DE/DS/MLE/PO for integrated and value-driven deliveries. Technical
Skills:
Advanced Data Modeling and Architecture: Expertise in star schema, dimensional modeling, medallion architecture, schema evolution management, and semantic layer design. Large‑Scale Semantic Layer & Data Model Optimization: Building and optimizing large-scale models, ensuring performance, consistency, and governance. Pipeline Orchestration and Automation: Experience with ETL/ELT tools like Azure Data Factory, Apache Airflow, Microsoft Fabric, Databricks Workflows. SQL Expertise & Performance Engineering: Proficiency in query tuning, partitioning, clustering, and handling large volumes of data with scalable SQL. Advanced Transformations with Python/Spark: Expertise in using PySpark, Spark SQL, and structured Python for advanced data transformations. CI/CD and Versioning for Data Pipelines: Experience with implementing CI/CD pipelines, version control, and DevOps best practices for data. Data Quality Frameworks & Observability: Rule definition, automated data validation, end-to-end monitoring, and observability. Data Governance, Cataloging, and Lineage: Practical use of tools like Microsoft Purview or Unity Catalog for governance, cataloging, and lineage tracing. BI Modeling and Performance: Advanced semantic modeling in Power BI, DAX optimization, and efficient analytical model design. Cloud Data Warehousing and Technical Architecture: Hands-on experience with platforms like BigQuery, Snowflake, Synapse, Delta Lake, ADLS, and Redshift; understanding system interdependencies.
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