Senior Data Engineer (Azure & Databricks) Job at Snaphunt, Alpharetta, GA

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  • Snaphunt
  • Alpharetta, GA

Job Description

Senior Data Engineer – Azure & Databricks

Industry: Technology Consulting / Digital Transformation

Role Level: Lead II – Software Engineering

Location: Alpharetta, Georgia (USA)

Employment Type: Full-Time

About the Role:

We are seeking an experienced Senior Data Engineer – Azure & Databricks with a proven track record (8+ years) of designing, building, and scaling modern data platforms within the Azure ecosystem.

This role is ideal for someone who thrives in complex enterprise environments, is deeply hands-on with Databricks and Spark, and can lead end-to-end data engineering initiatives from architecture to implementation.

You will be responsible for building robust, high-performance data pipelines, establishing engineering best practices, and collaborating with cross-functional teams to deliver reliable, scalable data solutions across both batch and real-time streams.

Key Responsibilities:

  • Architect, design, and implement scalable data platforms and pipelines using Azure and Databricks.
  • Build and optimise ingestion, transformation, and processing workflows for batch and real-time (streaming) use cases.
  • Work extensively with ADLS, Delta Lake, and Spark (Python/PySpark) to enable large-scale data engineering capabilities.
  • Lead the development of complex ETL/ELT pipelines, ensuring performance, reliability, and code quality.
  • Design conceptual, logical, and physical data models to support analytics and operational workloads.
  • Work with relational and lakehouse systems, including PostgreSQL and Delta Lake.
  • Define, implement, and enforce best practices for data governance, security, quality, and architecture standards.
  • Partner closely with architects, data scientists, analysts, and business teams to translate requirements into scalable technical solutions.
  • Troubleshoot production issues, drive performance optimisation, and support continuous platform improvements.
  • Mentor junior engineers and contribute to the creation of reusable components and engineering standards.

Required Qualifications:

  • 8+ years of hands-on data engineering experience in enterprise environments.
  • Strong expertise in Azure services, particularly Azure Databricks, Azure Functions, and (preferably) Azure Data Factory.
  • Advanced proficiency in Apache Spark with Python (PySpark).
  • Strong SQL skills, including query optimisation and performance tuning.
  • Deep experience with ETL/ELT methodologies, scheduling, and data orchestration.
  • Hands-on expertise with Delta Lake (ACID, schema evolution, performance tuning).
  • Strong understanding of data modelling (normalised, dimensional, lakehouse).
  • Proven experience with batch and streaming technologies such as Kafka or Event Hub.
  • Solid grasp of data architecture, distributed systems, and cloud-native design patterns.
  • Ability to design and evaluate end-to-end technical solutions and recommend best-fit architectures.
  • Excellent analytical, problem-solving, and communication skills.
  • Ability to collaborate across teams and lead technical discussions.

Preferred Skills:

  • Experience with CI/CD tools such as Azure DevOps and Git.
  • Familiarity with Infrastructure-as-Code (Terraform, ARM).
  • Exposure to data governance and metadata cataloguing tools (e.g., Azure Purview).
  • Experience supporting machine learning or BI workloads on Databricks.

Benefits:

For Full-Time, Regular Employees

  • Minimum 10 days paid vacation annually
  • 6 days paid sick leave (prorated for new hires)
  • 10 paid holidays
  • Paid bereavement and jury duty leave
  • Eligibility for 401(k) Retirement Plan with employer match
  • Medical, dental, and vision insurance eligibility (employee + dependents)
  • Company-paid:
  • Basic life insurance
  • Accidental death & disability coverage
  • Short- and long-term disability
  • Access to HSA and FSA programs (healthcare, dependent care, commuting)

Job Tags

Full time, Temporary work,

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