DATA ENGINEER (Software Engineering)

via Mediro ICT · Johannesburg, Gauteng

Pay
On application
Type
Contract · remote / hybrid
Experience
3–5 years
Level
Mid-level
Sector
Banking
Closes
in 30 days
Recruiter
Mediro ICT · All jobs at Mediro ICT

About the role

A data engineering role requiring strong software engineering skills to handle data pipelines, cloud services, and distributed processing.

2 Data Engineers = 1 Pure DE + 1 Software + Data Engineer

  • Production Support - Incident management, troubleshooting, RCA
  • Technical Documentation - Designs, mappings, runbooks, support documentation
  • Technical Leadership - Mentoring, technical reviews, design facilitation
  • Distributed Processing - Spark, PySpark, distributed processing
  • Streaming & Event Processing - Kafka, event streaming, event-driven processing
  • Data Warehousing - Warehouses, facts, dimensions, star/snowflake models
  • Data Lake - Data lakes, zones, partitioning, file formats
  • Lakehouse Engineering - Delta Lake, medallion patterns, lakehouse
  • AWS Data Services - S3, Glue, Lambda, Athena, Redshift, EMR
  • Serverless Data Engineering - Lambda, serverless processing
  • Messaging & Events - SQS, SNS, EventBridge, Kafka
  • Metadata & Lineage - Metadata, catalogues, lineage, traceability
  • Infrastructure as Code - Terraform, cloud provisioning
  • Performance Engineering - Partitioning, caching, query tuning, Spark optimisation
  • Operational Resilience - Recovery, availability, resilience, production readiness
  • Stakeholder Management - Communication, facilitation, requirements, technical decisions

What they're looking for

  • SQL Engineering - Advanced SQL, CTEs, window functions, query plans, optimisation
  • Python Engineering - Python, libraries, reusable modules, testing
  • ETL / ELT Engineering - ETL, ELT, ingestion, transformation, mappings
  • Data Modelling - Dimensional, relational, analytical, domain models
  • Cloud Data Engineering - AWS, Azure, managed data services
  • Data Orchestration - Airflow/MWAA, Step Functions, workflow orchestration
  • Data Quality - Profiling, validation, quality rules, exception handling
  • Data Reconciliation - Control totals, source-target comparison, balancing
  • Data Privacy & Security - IAM, encryption, access control, privacy
  • Data Observability - Logging, monitoring, alerting, metrics
  • Continuous Integration / Continuous Delivery (or Continuous Deployment).(CI/CD) for Data - Git, pipelines, automated deployment, quality gates
  • Automated Testing - Unit, integration, regression, reconciliation tests

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