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[GFA] Azure Senior Data Engineer at Software Mind · globalremotely

Company Description

Software Mind develops solutions that make an impact for companies around the globe. Tech giants & unicorns, transformative projects, emerging technologies and limitless opportunities – these are a few words that describe an average day for us. Building cross-functional engineering teams that take ownership and crave more means we’re always on the lookout for talented people who bring passion and creativity to every project. Our culture embraces openness, acts with respect, shows grit & guts and combines employment with enjoyment.

Job Description

Project – the aim you’ll have

Our customer provides innovative solutions and insights that enable our clients to manage risk and hire the best talent. Their advanced global technology platform supports fully scalable, configurable screening programs that meet the unique needs of over 33,000 clients worldwide. Headquartered in Atlanta, GA, they have an internationally distributed workforce spanning 19 countries with about 5,500 employees. Our partner perform over 93 million screens annually in over 200 countries and territories.

We are seeking a Senior Data Engineer with proven expertise in Databricks PySpark development, comprehensive data modeling experience (including fact/dimension tables, SCD Type 2 and incremental loads) and event-based architecture skills to join our Data Engineering Team and drive the evolution of our Azure-based Data Analytics Platform.

Position – how you’ll contribute

Develop reusable, metadata-driven data pipelines using Databricks Lakehouse architecture and PySpark
Design and implement comprehensive data models
Build robust ETL/ELT solutions with advanced features: Merge operations, SCD Type 2 implementations, etc.
Implement incremental data loads with idempotency patterns and overlap joins optimization
Automate and optimize data platform processes with focus on performance and reliability
Build integrations with data sources and consumers using event-driven patterns
Cooperate with infrastructure engineering team to set up cloud resources
Initiate and implement improvements to data platform architecture

Qualifications

Expectations – the experience you need

Databricks expertise: proficient in Databricks Lakehouse architecture and PySpark development
Data modeling mastery: extensive experience in data model definition including fact tables, dimension tables, measures, grain analysis, SCD Type 2, surrogate keys, late-arriving records handling, overlap joins, and incremental loads with idempotency
Programming: advanced Python and PySpark skills for ETL/ELT development
Databricks optimization: deep knowledge of optimize, zOrder, Liquid clustering, ACID transactions and performance tuning
Event-based architecture: proven experience in designing and implementing event-driven data solutions
Azure data platform: experience working with Azure-based datasets and data pipelines
SQL proficiency: strong SQL skills for complex data transformations
Large-scale data processing: experienced in handling large and complex datasets efficiently
CI/CD: experience in developing automated deployment pipelines
Networking fundamentals: understanding of basic networking concepts
Agile methodology: familiar with Scrum and agile development practices

Additional skills – the edge you have

Understanding of stream processing challenges and Spark Structured Streaming
Experience with Infrastructure as Code (Terraform, Bicep)
Experience with containerized applications (Azure Container Apps, Kubernetes)
Knowledge of Azure cloud native solutions (Azure Data Factory, Azure Function App, Azure Container Instances)