Lead Data/AI Engineering

Job Description:

Job Description – External:

We are hiring a Senior Data Engineer with deep expertise in Azure Data Bricks, Azure Data Lake, and Azure Synapse Analytics to join our high-performing team. The ideal candidate will have a proven track record in designing, building, and optimizing big data pipelines and architectures while leveraging their technical proficiency in cloud-based data engineering.

This role requires a strategic thinker who can bridge the gap between raw data and actionable insights, enabling data-driven decision-making for large-scale enterprise initiatives. A strong foundation in distributed computing, ETL frameworks, and advanced data modeling is crucial. The individual will work closely with data architects, analysts, and business teams to deliver scalable and efficient data solutions.

Key Responsibilities:

Data Engineering & Architecture:

• Design, develop, and maintain high-performance data pipelines for structured and unstructured data using Azure Data Bricks and Apache Spark.
• Build and manage scalable data ingestion frameworks for batch and real-time data processing.
• Implement and optimize data lake architecture in Azure Data Lake to support analytics and reporting workloads.
• Develop and optimize data models and queries in Azure Synapse Analytics to power BI and analytics use cases.

Cloud-Based Data Solutions:

• Architect and implement modern data lakehouses combining the best of data lakes and data warehouses.
• Leverage Azure services like Data Factory, Event Hub, and Blob Storage for end-to-end data workflows.
• Ensure security, compliance, and governance of data through Azure Role-Based Access Control (RBAC) and Data Lake ACLs.

ETL/ELT Development:

• Develop robust ETL/ELT pipelines using Azure Data Factory, Data Bricks notebooks, and PySpark.
• Perform data transformations, cleansing, and validation to prepare datasets for analysis.
• Manage and monitor job orchestration, ensuring pipelines run efficiently and reliably.

Performance Optimization:

• Optimize Spark jobs and SQL queries for large-scale data processing.
• Implement partitioning, caching, and indexing strategies to improve performance and scalability of big data workloads.
• Conduct capacity planning and recommend infrastructure optimizations for cost-effectiveness.

Collaboration & Stakeholder Management:

• Work closely with business analysts, data scientists, and product teams to understand data requirements and deliver solutions.
• Participate in cross-functional design sessions to translate business needs into technical specifications.
• Provide thought leadership on best practices in data engineering and cloud computing.

Documentation & Knowledge Sharing:

• Create detailed documentation for data workflows, pipelines, and architectural decisions.
• Mentor junior team members and promote a culture of learning and innovation.

Required Qualifications:

• Experience:
  • 12+ years of experience in data engineering, big data, or cloud-based data solutions.
  • Proven expertise with Azure Data Bricks, Azure Data Lake, and Azure Synapse Analytics.
• Technical Skills:
  • Strong hands-on experience with Apache Spark and distributed data processing frameworks.
  • Advanced proficiency in Python and SQL for data manipulation and pipeline development.
  • Deep understanding of data modeling for OLAP, OLTP, and dimensional data models.
  • Experience with ETL/ELT tools like Azure Data Factory or Informatica.
  • Familiarity with Azure DevOps for CI/CD pipelines and version control.
• Big Data Ecosystem:
  • Familiarity with Delta Lake for managing big data in Azure.
  • Experience with streaming data frameworks like Kafka, Event Hub, or Spark Streaming.
• Cloud Expertise:
  • Strong understanding of Azure cloud architecture, including storage, compute, and networking.
  • Knowledge of Azure security best practices, such as encryption and key management.

Preferred Skills (Nice to Have):

• Experience with machine learning pipelines and frameworks like MLFlow or Azure Machine Learning.
• Knowledge of data visualization tools such as Power BI for creating dashboards and reports.
• Familiarity with Terraform or ARM templates for infrastructure as code (IaC).
• Exposure to NoSQL databases like Cosmos DB or MongoDB.
• Experience with data governance tools like Azure Purview.

Weekly Hours:

Time Type:

Location:

It is the policy of AT&T to provide equal employment opportunity (EEO) to all persons regardless of age, color, national origin, citizenship status, physical or mental disability, race, religion, creed, gender, sex, sexual orientation, gender identity and/or expression, genetic information, marital status, status with regard to public assistance, veteran status, or any other characteristic protected by federal, state or local law. In addition, AT&T will provide reasonable accommodations for qualified individuals with disabilities. AT&T is a fair chance employer and does not initiate a background check until an offer is made.