Senior Data Engineer
Location: Frisco, TX
Experience: 8+ Years
Domain: Finance / Revenue / Billing / Data & Intelligence
Job Summary
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.
Key Responsibilities
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.
Required Skills
- 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.
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