Senior Data Engineer

We are seeking a highly experienced Senior Data Engineer to join the Data & Intelligence team supporting T-Mobile Finance / RDMP. The ideal candidate will have strong expertise in designing and developing enterprise-scale data platforms and pipelines across Snowflake, Databricks, PySpark, Python, Advanced SQL, and Azure. The candidate will provide technical leadership across data engineering, real-time processing, data quality, DevOps, security, governance, and finance/revenue data integration. Strong experience with billing, revenue, GL, Opex, reconciliation, and financial reporting data is highly preferred., Data Pipeline Development

• Architect, design, and oversee development of enterprise-scale ELT/ETL pipelines for finance and revenue data, including billing, revenue, GL, and Opex.
• Define standards for batch, incremental, CDC, watermarking, and event-driven ingestion patterns.
• Design idempotent, fault-tolerant, highly scalable, and production-ready pipelines.
• Establish frameworks for error handling, retry strategies, dead-letter queues, and operational resiliency.
• Provide technical leadership for high-volume, multi-source data integration.

Platform & Tooling

• Lead architecture and adoption of Snowflake and Databricks for large-scale data processing and analytics.

• Establish best practices for:

• Snowflake: Snowpipe, Streams, Tasks, query optimization, and cost efficiency.
• Databricks: PySpark, Delta Live Tables, Unity Catalog, and job optimization.
• dbt: Modular design, testing frameworks, CI/CD integration, and reusable components.
• Establish and govern orchestration frameworks using Airflow and/or Azure Data Factory.
• Define DAG standards, dependencies, monitoring, and operational best practices.
• Evaluate and drive platform and tooling standardization across engineering teams.

Cloud Infrastructure

• Architect and optimize cloud-native data platforms on Azure, including:

• ADLS Gen2
• Event Hub
• Azure Data Factory
• Key Vault
• Define standards for Infrastructure as Code using Terraform and/or Bicep.
• Drive cloud cost optimization through compute sizing, storage design, partitioning, and workload isolation.
• Ensure data platforms are scalable, secure, resilient, and production-ready.

Languages & Data Processing

• Provide technical leadership in Advanced SQL, Python, and PySpark.
• Develop and optimize complex transformations and distributed data processing workloads.
• Guide engineering teams on reusable frameworks, coding standards, and performance optimization.
• Provide oversight for Spark, Scala where applicable, and automation scripting.

Streaming & Real-Time Data

• Architect real-time and near-real-time data processing solutions using Kafka, Azure Event Hub, and Spark Structured Streaming.
• Define standards for stateful processing, watermarking, checkpointing, and fault tolerance.

• Lead real-time finance and revenue use cases such as:

• Reconciliation
• Anomaly detection
• Operational reporting
• Data monitoring

Data Quality & Testing

• Establish enterprise frameworks for data quality, validation, testing, and observability.
• Define standards for automated unit, integration, and regression testing.
• Implement data validation for completeness, accuracy, consistency, and freshness.
• Utilize dbt tests, Great Expectations, and custom data quality frameworks.
• Establish SLA monitoring, alerting, and data freshness tracking across pipelines.
• Drive proactive data quality and governance practices.

Data Modeling

• Interpret and implement architect-defined enterprise data models, including Star Schema, Snowflake Schema, and Data Vault.

• Provide guidance on:

• SCD Type 1 and Type 2
• Partitioning
• Clustering
• Performance optimization
• Collaborate with data architects to evolve scalable and reusable data models.
• Support semantic layer enablement for analytics and reporting.

DevOps & Engineering Practices

• Define and enforce CI/CD standards for data engineering using GitHub Actions and/or Azure DevOps.
• Establish code quality, versioning, branching, pull request, and deployment standards.
• Standardize environment promotion across Dev QA Production.
• Establish reusable frameworks, templates, and engineering best practices.
• Drive continuous improvement and engineering excellence across teams.

Security & Governance

• Lead implementation of enterprise-grade security and governance controls.

• Implement and govern:

• RBAC
• Row-level and column-level security
• PII and CPNI compliance
• TISS-310 controls
• Define standards for secrets management and secure pipeline development.
• Ensure data lineage, auditability, security, and compliance readiness.

Finance Domain Expertise

• Apply strong understanding of finance and revenue data domains, including:

• Billing and revenue systems
• General Ledger (GL)
• Financial reporting
• Revenue recognition
• Revenue reconciliation
• Period-end close processes
• Guide engineering teams in accurately implementing finance-related business logic.
• Ensure high data integrity and reliability for regulated financial data., Principal Data Engineer Data & Intelligence Location : Bellevue/ Frisco Rate : Open DATA PIPELINE DEVELOPMENT Architect, design, and oversee development of enterprise-scale…
• 15 hours ago
• Apply easily

• 8+ years of experience in Data Engineering.
• Strong hands-on experience with Snowflake and Databricks.
• Expert-level PySpark, Python, and Advanced SQL skills.
• Strong experience designing enterprise-scale ETL/ELT pipelines.
• Experience with Azure data services, particularly ADLS Gen2, ADF, Event Hub, and Key Vault.
• Experience with Kafka and/or Event Hub and Spark Structured Streaming.
• Strong experience with dbt and/or Airflow.
• Experience with Terraform and/or Bicep.
• Strong understanding of CI/CD and DevOps practices.
• Experience with data quality, observability, testing, and governance.
• Strong understanding of enterprise data modeling and SCD methodologies.
• Excellent troubleshooting, performance tuning, and production support experience.

Preferred Qualifications

• Experience working with telecom, finance, revenue, billing, or GL data.
• Experience with large-scale financial data platforms.
• Experience with revenue reconciliation and revenue recognition processes.
• Experience with CPNI/PII compliance and TISS-310.
• Experience establishing enterprise data engineering standards and reusable frameworks.
• Strong technical leadership and architecture experience.

Soft Skills

• Strong technical leadership and decision-making abilities.
• Ability to act as a technical escalation point across engineering teams.
• Excellent collaboration skills with architects, product managers, analysts, and business stakeholders.
• Ability to communicate complex technical concepts clearly to technical and non-technical audiences.
• Strong problem-solving and incident management skills.
• Experience leading incident reviews and driving continuous improvement.