Key Responsibilities
- Architect and develop scalable ETL/ELT pipelines for finance and revenue data.
- Lead implementation of Databricks, Snowflake, dbt, and orchestration frameworks (Airflow/ADF).
- Design cloud-native data platforms on Azure (ADLS Gen2, ADF, Event Hub, Key Vault) or AWS.
- Build real-time data processing solutions using Kafka/Event Hub and Spark Structured Streaming.
- Establish enterprise standards for data quality, observability, testing, CI/CD, and governance.
- Support scalable data modeling (Star Schema, Snowflake, Data Vault, SCD Type 1/2).
- Drive security, RBAC, data lineage, compliance, and platform modernization initiatives.
- Mentor engineering teams and provide technical leadership across cross-functional programs.
Required Skills
- 12+ years of Data Engineering experience with enterprise-scale platforms.
- Strong expertise in Databricks, Snowflake, PySpark, Python, Advanced SQL, Spark.
- Experience with Azure Data Factory, ADLS Gen2, Event Hub, Azure DevOps, or AWS equivalents.
- Hands-on experience with Airflow, CI/CD, GitHub Actions, and Infrastructure as Code (Terraform/Bicep).
- Strong knowledge of streaming technologies (Kafka/Event Hub, Spark Structured Streaming).
- Experience with data quality frameworks, monitoring, governance, and performance optimization.
- Strong understanding of data modeling, Delta Lake, and cloud-native architectures.
- Excellent leadership, mentoring, and stakeholder management skills.
Preferred Qualifications
- Finance domain expertise (Billing, Revenue, GL, Revenue Recognition, Period-End Close).
- Experience with dbt, Great Expectations, Unity Catalog, and Microsoft Fabric.
- Background in large enterprise or FAANG environments with platform architecture leadership.
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