Job Description

Job Title: Lead Data Engineer
Location: Irving, TX - Hybrid

Must have:

• Expert in Databricks, PySpark and Azure data services like ADF, Synapse etc.
• Must have lead level experience
• 8+ years of professional data engineering experience, with proven technical leadership.
• Deep understanding of Azure tech stack (Databricks, ADF, Key Vault, ASQL, ADLS Gen2, Service Principles etc.)
• Deep experience with Databricks (design, optimization, notebooks, stored procedures, UDFs, etc).
• Hands-on experience with ETL tools such as SSIS and Azure Data Factory.
• Experience working with API calls and Ingesting data from external source systems
• Experience with SQL, Python and PySpark
• Experience with building dynamic and scalable solutions for data ingestion and curation
• Experience in data engineering and MS SQL Server and T-SQL development.
• Strong understanding of data lake/delta lake architectures and modern data pipelines.
• Strong understanding of medallion architectures and design patterns.
• Strong understanding of data modeling techniques.
• Experience handling large-scale data volumes (millions of records).
• Proficiency in building and maintaining CI/CD pipelines.
• Experience with IaC technologies such as Terraform or Pulumi.
• Exposure to big data tools (Spark, Hadoop, Kafka) preferred.
• Experience with reporting tools such as Power BI or Tableau a plus.
• Knowledge of .NET development beneficial for cross-team collaboration.
• Prior experience working in an Agile development environment required.
• Strong communication and collaboration skills with the ability to translate technical concepts to business partners.

What Success Looks Like:

• You're the go-to technical lead for all things related to data design, integration, and performance.
• You enable consistency and best practices across Azure, Databricks, SQL, ETL, and data engineering domains.
• You mentor others while remaining hands-on with architecture and solution delivery.
• You ensure the Internal Analytics platform continues to evolve as a reliable, scalable data source for the organization.

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