Data Engineer

Apptad Inc; Frisco (TX); On-site; USD 140,000 - 190,000; Full time

Job summary

Apptad Inc. is seeking a Senior Data Engineer to join the Data & Intelligence team supporting T-Mobile Finance/RDMP. The role requires building enterprise-scale data platforms using Snowflake, Databricks, PySpark, Python, and Azure.

You will lead data engineering, real-time processing, data quality, and governance for finance/revenue data integration. The position emphasizes architecture and production-readiness across pipelines, security, and cost optimization with a focus on billing, revenue,

Qualifications

• 8+ years of experience in Data Engineering and data platform development.
• Strong expertise in designing and building enterprise-scale ELT/ETL pipelines for finance/revenue data.
• Experience with Snowflake and Databricks for large-scale data processing.
• Proficient in PySpark, Python, and Advanced SQL.
• Hands-on with Azure data services (ADLS Gen2, ADF, Event Hub, Key Vault).
• Familiar with real-time streaming (Kafka/Event Hub) and Spark Structured Streaming.
• Knowledge of dbt, Airflow, Terraform or Bicep, and CI/CD practices.

Responsibilities

• Architect and oversee development of enterprise-scale ELT/ETL pipelines for finance and revenue data.
• Define standards for batch, incremental, CDC, watermarking, and event-driven ingestion patterns.
• Lead architecture and adoption of Snowflake and Databricks for large-scale data processing.
• Establish governance, security, and data quality practices across platforms.
• Provide technical leadership for real-time processing, DevOps, and data integration initiatives.

Skills

Analytical thinking

Technical leadership

Communication

Tools

Snowflake

Databricks

PySpark

Python

Advanced SQL

Azure

Kafka

Airflow

dbt

Terraform

Bicep

GitHub Actions

Azure DevOps

Job description

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.