Data Engineer

Data Engineer with Data Bricks with Pyspark and SQL knowledgeData Engineer with Power BI & Fabric  Location:- Bellevue, WA - Onsite 3 Days - ONLY LOCALSMandatory Areas: Data Bricks with Pyspark and SQL knowledge Must have Skills – Data Engineering, Data Pipeline, Apache Spark, Databricks, Big Data, Data Processing, SQL Skill 1 – 7Yrs of Exp – Databricks, Pyspark Skill 2 – 7Yrs of Exp – SQL, Spark, BigData Skill 3 – 5Yrs of Exp in Telecom DomainJOB SUMMARY • This role builds, and maintains scalable data pipelines and lakehouse infrastructure on Microsoft Azure to support efficient extraction, transformation, and loading of data across batch and real-time workloads. It involves implementing and managing the Medallion Architecture (Bronze → Silver → Gold) using Azure Data Factory, Databricks-PySpark, and Azure SQL Database and Databricks unity catalogue. • The role requires ensuring SLA-adherent data quality standards. Success is measured by pipeline reliability, data freshness SLA compliance, and the quality of Gold-layer datasets powering Power BI executive dashboards. • The work supports organizational decision-making by delivering trusted, well-governed data to business executives and analytics consumers. Required Skills: • Experience building and optimizing big data pipelines using Azure Data Factory, PySpark, and SQL across structured and semi-structured data sets • Hands-on experience implementing Medallion Architecture (Bronze/Silver/Gold) • Experience with Delta Lake — ACID transactions, incremental loading, schema evolution, partitioning strategies • Experience performing root cause analysis on pipeline failures and data quality issues to resolve SLA breaches and identify platform improvement opportunities Azure Foundational Services : • Working knowledge of: Azure Data Factory (ADF), ADLS Gen2, Azure SQL Database, Azure Blob Storage, Azure Key Vault, Azure Monitor / Log Analytics, Azure Event Hubs, Microsoft Fabric Lakehouse, Azure Active Directory / Entra ID (RBAC, Service Principals) Programming Languages: • Proficiency in Python and PySpark for data transformation, pipeline automation, and large-scale distributed processing; strong SQL skills including window functions, CTEs, and query optimization across relational and lakehouse engines Data Architecture: • Solid understanding of Medallion Architecture, dimensional modeling (Star Schema, SCD Types 1/2/3), and the trade-offs between lakehouse, data warehouse, and data lake patterns Pipeline Engineering: • Ability to build robust ADF pipelines with ForEach, Lookup, Copy Activity, and Data Flows; incremental loading via watermark or CDC; error handling, retry logic, and dead-letter patterns Data Quality Experience: • Experience implementing SLA-based data quality checks (freshness, completeness, row count), monitoring via Azure Monitor and ADF diagnostic logs, and defining data quality agreements with business stakeholders. DevOps for Data: • Experience with Git-based workflows, ADF Git integration, CI/CD pipeline promotion across Dev/Test/Prod using Azure DevOps or GitHub Actions Reporting Layer Awareness: • Understanding of how Gold-layer data feeds Power BI — DirectQuery vs. Import mode trade-offs, dataset refresh patterns, and semantic model collaboration with BI teams • Ability to manage work across multiple concurrent pipeline projects, prioritize by business impact, and communicate status clearly to technical and non-technical stakeholders Good to have skills: • Experience with Microsoft Fabric (Lakehouse, Notebooks, OneLake, Fabric Pipelines) — active migration or greenfield project • Experience with real-time / streaming workloads using Azure Event Hubs or Structured Streaming in PySpark • Experience delivering data platforms for executive-level reporting via Power BI semantic models