Software Engineer
Insight Global
Required Skills & Experience
Expertise in big data technologies (Hadoop, Spark, Kafka)
Advanced SQL proficiency for investigation and optimization
Strong Python development skills
Experience with cloud platforms (AWS or Azure)
Knowledge of Snowflake and dimensional modeling
Experience with DevOps tooling (CI/CD, Docker, Kubernetes)
Familiarity with ML frameworks (TensorFlow, PyTorch)
Strong debugging and analytical skills
Nice to Have Skills & Experience
Leadership and mentorship
Effective communication with technical and non‑technical audiences
Problem‑solving and critical thinking
Ownership and accountability
Adaptability and continuous learning
Job Description
The Data Engineer is responsible for architecting, developing, and maintaining enterprise‑scale data platforms that support analytics, operational reporting, and machine learning initiatives. This role requires deep technical expertise in distributed systems, cloud platforms, and data modeling, combined with strong communication and leadership capabilities.
Design and implement large‑scale ETL/ELT pipelines using Python, Spark, and distributed processing frameworks
Develop and maintain big data infrastructure leveraging Hadoop, Spark, Kafka, and Kafka Streams
Architect cloud‑native data solutions on AWS or Azure, including serverless components
Build and optimize Snowflake data warehouses using dimensional modeling best practices
Conduct data discovery and source analysis to support new integrations and transformations
Model complex datasets using normalized, denormalized, star, and snowflake schemas
Integrate external systems through RESTful APIs and automated ingestion frameworks
Implement DevOps practices including CI/CD, containerization, and orchestration
Develop real‑time streaming applications using Kafka, Storm, Kinesis, or Pub/Sub
Operationalize machine learning models in collaboration with data science teams
Optimize performance across queries, pipelines, and distributed workloads
Troubleshoot and resolve complex data issues across upstream and downstream systems
- Maintain comprehensive documentation for data pipelines, models, and integrations
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