ETL Data Engineer

Lead Data Engineer We are seeking a Lead Data Engineer with 5+ years of experience to architect and scale modern data platforms, lead cloud data engineering initiatives, and deliver robust, secure, and high-performance data solutions. The role focuses on designing and developing next-generation cloud data lake and analytics platforms to support personalized customer experiences and enterprise analytics.

Primary Skills Big Data, Oracle, Azure, Snowflake Technical Skills Data Engineering, Big Data, Platform Engineering, Multi-tenant Database Design, Data Modeling, Advanced SQL, Oracle, Azure SQL Managed Instance (SQL MI), MySQL, Snowflake, Azure, ETL, ELT, Informatica, SnapLogic, Azure Functions, Python, Batch Data Processing, Feature Engineering, GitHub, CI/CD, DataOps, Azure Event Hubs, Kafka, Amazon Kinesis, Confluence, Draw.io, Metadata Management, Data Lineage, Production Support, Incident Management, Root Cause Analysis (RCA), Financial Services, Benefits Administration, Data Architecture, Batch Pipelines, Streaming Pipelines, Lakehouse, Delta Lake, Apache Iceberg, Apache Hudi, Data Warehouse, Semantic Data Modeling, Azure Data Factory, Azure Databricks, Apache Spark, Azure Kubernetes Service (AKS), Azure Service Bus (ASB), API Management, Azure Storage Accounts, Azure Event Grid, Azure Redis Cache, SQL Server, PostgreSQL, Cosmos DB, MongoDB, Azure DevOps, GitHub Actions, Unit Testing, Integration Testing, Data Quality, Great Expectations, Deequ, Monte Carlo, Schema Evolution, Partitioning, Performance Tuning, Business Intelligence, Analytics, Agile, Scrum, Jira, dbt, Control-M, DB2, CICS, Kubernetes, Containerization, Infrastructure as Code, Terraform, ARM Templates, Bicep.

Roles and Responsibilities Architect scalable cloud data platforms, design and implement high-performance batch and streaming data pipelines, build ETL/ELT solutions using Informatica, SnapLogic, Azure Data Factory, Azure Functions, Python and Databricks, develop lakehouse and data warehouse solutions, create scalable semantic data models, optimize SQL queries and database performance, implement DataOps and CI/CD practices, automate deployments and data quality validation, design metadata