Sr Data Engineer – Data & Intelligence
Job Location:
Job Location:
Frisco, TX - USA
Monthly Salary:
Not Disclosed
Monthly Salary:
Posted on:
3 hours ago
Posted on:
Vacancies:
1 Vacancy
Vacancies:
Job Summary
Job Summary
We are seeking a Senior Data Engineer to design build and operate highly scalable batch and streaming data pipelines supporting T Mobiles Finance and Intelligence platforms. This role requires deep expertise in modern cloud data stacks (Snowflake Databricks dbt) strong SQL/Python skills and solid understanding of finance data domains including billing revenue GL and OPEX. The ideal candidate owns complex pipelines end to end mentors junior engineers and helps drive platform standards and best practices.
Key Responsibilities
Data Pipeline Development
Design and build scalable reliable ELT/ETL pipelines for finance data (billing revenue GL OPEX).
Implement batch and incremental ingestion patterns (full load CDC watermark-based).
Build idempotent rerunnable pipelines with robust error handling retry logic and dead-letter queue patterns.
Platform & Tooling
Develop and optimize pipelines using Snowflake (Snowpipe Streams Tasks Dynamic Tables performance tuning).
Build data processing workflows in Databricks (PySpark Delta Live Tables Unity Catalog job clusters).
Create and maintain dbt models tests snapshots macros and packages with CI integration.
Orchestrate data workflows using Airflow or Azure Data Factory (DAG design dependencies scheduling alerts).
Cloud Infrastructure
Work within Azure (ADLS Gen2 Event Hub ADF Azure Functions Key Vault) and/or AWS (S3 Glue Lambda Secrets Manager).
Apply Infrastructure as Code fundamentals (Terraform Bicep) for pipeline and resource provisioning.
Apply cloud cost awareness including compute sizing partitioning strategies and storage optimization.
Languages & Frameworks
Write advanced SQL (CTEs window functions query tuning execution plan analysis).
Develop in Python (pandas PySpark requests pytest logging).
Read and modify existing Scala/Spark jobs as needed.
Use shell scripting for automation and operational tasks.
Streaming & Real Time Processing
Build near real time pipelines using Apache Kafka / Azure Event Hub.
Implement Spark Structured Streaming with stateful aggregations watermarking and checkpointing.
Support finance use cases such as revenue reconciliation and fraud signal feeds.
Data Quality & Testing
Implement unit and integration testing for pipelines (pytest dbt tests).
Create data quality checks (row counts nulls duplicates referential integrity).
Use Great Expectations or custom frameworks for validation.
Monitor SLAs for pipeline latency and data freshness with alerting.
Data Modeling Support
Implement architected schemas (star snowflake data vault).
Manage Slowly Changing Dimensions (SCD Type 1 & 2) for finance entities.
Define partitioning and clustering strategies for large-scale finance tables.
Support semantic layer definitions (metrics and dimensions).
DevOps & Engineering Practices
Participate in CI/CD for data pipelines using GitHub Actions or Azure DevOps.
Follow Git branching strategies (trunk-based feature branches).
Perform code reviews and enforce engineering standards.
Support environment promotion patterns (dev QA prod).
Security & Governance
Implement RBAC and row/column-level security in Snowflake and Databricks.
Ensure PII and CPNI handling per T Mobile TISS 310 policy.
Manage secrets securely (Key Vault environment variables no hardcoded credentials).
Implement data lineage and audit instrumentation for compliance.
Collaboration & Communication
Partner with Data Architects to translate design specs into production-ready pipelines.
Work closely with Data Analysts to optimize downstream consumption performance.
Communicate pipeline incidents and data issues clearly to business stakeholders.
Participate in on-call rotation to support production pipelines.
Senior-Level Expectations
Own delivery of complex multi-source pipelines with minimal direction.
Mentor junior and mid-level data engineers through pairing and code reviews.
Identify and drive technical debt reduction alongside feature delivery.
Contribute to and shape team standards templates and reusable components.
Influence tooling framework and platform decisions across the team.
Required Qualifications
8 years of experience in data engineering or platform engineering roles.
Strong experience with Snowflake Databricks and dbt in production environments.
Advanced SQL and Python skills.
Experience building finance or regulated data pipelines at scale.
Preferred Qualifications
Telecom industry experience (ARPU churn prepaid/postpaid metrics).
Experience with both Azure and AWS cloud platforms.
Prior experience supporting financial reporting and period-end close cycles.
We are seeking a Senior Data Engineer to design build and operate highly scalable batch and streaming data pipelines supporting T Mobiles Finance and Intelligence platforms. This role requires deep expertise in modern cloud data stacks (Snowflake Databricks dbt) strong SQL/Python skills and solid understanding of finance data domains including billing revenue GL and OPEX. The ideal candidate owns complex pipelines end to end mentors junior engineers and helps drive platform standards and best practices.
Key Responsibilities
Data Pipeline Development
Design and build scalable reliable ELT/ETL pipelines for finance data (billing revenue GL OPEX).
Implement batch and incremental ingestion patterns (full load CDC watermark-based).
Build idempotent rerunnable pipelines with robust error handling retry logic and dead-letter queue patterns.
Platform & Tooling
Develop and optimize pipelines using Snowflake (Snowpipe Streams Tasks Dynamic Tables performance tuning).
Build data processing workflows in Databricks (PySpark Delta Live Tables Unity Catalog job clusters).
Create and maintain dbt models tests snapshots macros and packages with CI integration.
Orchestrate data workflows using Airflow or Azure Data Factory (DAG design dependencies scheduling alerts).
Cloud Infrastructure
Work within Azure (ADLS Gen2 Event Hub ADF Azure Functions Key Vault) and/or AWS (S3 Glue Lambda Secrets Manager).
Apply Infrastructure as Code fundamentals (Terraform Bicep) for pipeline and resource provisioning.
Apply cloud cost awareness including compute sizing partitioning strategies and storage optimization.
Languages & Frameworks
Write advanced SQL (CTEs window functions query tuning execution plan analysis).
Develop in Python (pandas PySpark requests pytest logging).
Read and modify existing Scala/Spark jobs as needed.
Use shell scripting for automation and operational tasks.
Streaming & Real Time Processing
Build near real time pipelines using Apache Kafka / Azure Event Hub.
Implement Spark Structured Streaming with stateful aggregations watermarking and checkpointing.
Support finance use cases such as revenue reconciliation and fraud signal feeds.
Data Quality & Testing
Implement unit and integration testing for pipelines (pytest dbt tests).
Create data quality checks (row counts nulls duplicates referential integrity).
Use Great Expectations or custom frameworks for validation.
Monitor SLAs for pipeline latency and data freshness with alerting.
Data Modeling Support
Implement architected schemas (star snowflake data vault).
Manage Slowly Changing Dimensions (SCD Type 1 & 2) for finance entities.
Define partitioning and clustering strategies for large-scale finance tables.
Support semantic layer definitions (metrics and dimensions).
DevOps & Engineering Practices
Participate in CI/CD for data pipelines using GitHub Actions or Azure DevOps.
Follow Git branching strategies (trunk-based feature branches).
Perform code reviews and enforce engineering standards.
Support environment promotion patterns (dev QA prod).
Security & Governance
Implement RBAC and row/column-level security in Snowflake and Databricks.
Ensure PII and CPNI handling per T Mobile TISS 310 policy.
Manage secrets securely (Key Vault environment variables no hardcoded credentials).
Implement data lineage and audit instrumentation for compliance.
Collaboration & Communication
Partner with Data Architects to translate design specs into production-ready pipelines.
Work closely with Data Analysts to optimize downstream consumption performance.
Communicate pipeline incidents and data issues clearly to business stakeholders.
Participate in on-call rotation to support production pipelines.
Senior-Level Expectations
Own delivery of complex multi-source pipelines with minimal direction.
Mentor junior and mid-level data engineers through pairing and code reviews.
Identify and drive technical debt reduction alongside feature delivery.
Contribute to and shape team standards templates and reusable components.
Influence tooling framework and platform decisions across the team.
Required Qualifications
8 years of experience in data engineering or platform engineering roles.
Strong experience with Snowflake Databricks and dbt in production environments.
Advanced SQL and Python skills.
Experience building finance or regulated data pipelines at scale.
Preferred Qualifications
Telecom industry experience (ARPU churn prepaid/postpaid metrics).
Experience with both Azure and AWS cloud platforms.
Prior experience supporting financial reporting and period-end close cycles.
