// INDEPENDENT CONSULTANT

Building scalable
data infrastructure

Expert data engineering solutions that transform raw data into actionable insights.

5+
Years experience
svetortjenester@gmail.com linkedin.com/in/sverre-anders-torp-39040851/ github.com/gitders222
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Data Pipeline Development

Design and build robust ETL/ELT pipelines that reliably move and transform data at scale. Batch and streaming architectures optimized for performance and cost.

Apache Airflow Spark Kafka
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Cloud Data Architecture

Architecting modern data platforms on AWS, GCP, or Azure. Data lakes, warehouses, and lakehouse implementations with governance and security built-in.

Snowflake Databricks BigQuery
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Real-Time Analytics

Building streaming data platforms that process and analyze data in real-time. Event-driven architectures for instant insights and automated decision-making.

Kafka Streams Flink ksqlDB
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Data Quality & Observability

Implementing comprehensive data quality frameworks and monitoring systems. Ensure data reliability with automated validation, testing, and incident response.

Great Expectations dbt Monte Carlo
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Infrastructure Optimization

Performance tuning and cost optimization for existing data systems. Identify bottlenecks, reduce cloud spend, and improve query performance.

Query Optimization Cost Analysis Scalability
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Team Enablement

Training and mentoring your data teams on best practices, modern tools, and efficient workflows. Knowledge transfer that builds lasting capability.

Workshops Documentation Best Practices
PROJECT_01

Real-Time E-commerce Analytics Platform

Designed and implemented a streaming analytics platform processing 50M+ daily events. Built on Kafka and Flink with sub-second latency for inventory and pricing optimization.

Apache Kafka Apache Flink AWS PostgreSQL
50M+
Events per day
<1s
Processing latency
PROJECT_02

Healthcare Data Lakehouse Migration

Migrated legacy data warehouse to modern lakehouse architecture on Databricks. Unified batch and streaming workloads while reducing infrastructure costs by 40%.

Databricks Delta Lake Apache Spark Azure
40%
Cost reduction
5TB
Data migrated
PROJECT_03

ML Pipeline Infrastructure for FinTech

Built automated feature engineering and model training pipelines. Orchestrated with Airflow, enabling data scientists to deploy models 10x faster.

Apache Airflow MLflow Python GCP
10x
Faster deployment
100+
Features engineered

Processing & Compute

  • Apache Spark
  • Apache Flink
  • dbt (data build tool)
  • Python / PySpark
  • Scala

Streaming & Messaging

  • Apache Kafka
  • Kafka Streams
  • Apache Pulsar
  • AWS Kinesis
  • Google Pub/Sub

Orchestration

  • Apache Airflow
  • Prefect
  • Dagster
  • AWS Step Functions
  • Azure Data Factory

Storage & Warehousing

  • Snowflake
  • Databricks
  • Google BigQuery
  • Amazon Redshift
  • Delta Lake

Cloud Platforms

  • AWS (S3, EMR, Glue)
  • Google Cloud Platform
  • Microsoft Azure
  • Terraform / IaC
  • Kubernetes

Data Quality & Monitoring

  • Great Expectations
  • Monte Carlo
  • Datadog
  • Grafana
  • Custom frameworks

Let's Build Something

Available for consulting projects, architecture reviews, and team augmentation. Let's discuss how I can help solve your data challenges.

@
Email
svetortjenester@gmail.com
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LinkedIn
in/sverre-anders-torp-39040851
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GitHub
@gitders222
PROJECT_01

Real-Time E-commerce Analytics Platform

Streaming analytics platform processing 50M+ daily events with sub-second latency

// Overview

Built a comprehensive real-time analytics platform for a major e-commerce retailer to process and analyze customer behavior, inventory movements, and pricing dynamics. The system handles 50+ million events daily with sub-second latency, enabling instant business decisions.

// The Challenge

  • Legacy batch processing couldn't support real-time inventory optimization
  • Pricing decisions were delayed by 24-48 hours due to slow data pipelines
  • Multiple data silos created inconsistent analytics across departments
  • Black Friday traffic spikes caused system failures and data loss

// Solution Delivered

  • Architected event-driven platform using Apache Kafka for data ingestion
  • Implemented Apache Flink for real-time stream processing and transformations
  • Built custom state management for session tracking and user journeys
  • Designed fault-tolerant architecture with automatic failover and recovery
  • Created real-time dashboards using TimescaleDB and Grafana
  • Implemented A/B testing framework integrated into the data pipeline

// Impact Metrics

50M+
Events processed daily
<1s
End-to-end latency
99.99%
System uptime
18%
Revenue increase

// Technology Stack

Apache Kafka Apache Flink AWS MSK AWS EMR PostgreSQL TimescaleDB Redis Python Scala Terraform Grafana Prometheus
PROJECT_02

Healthcare Data Lakehouse Migration

Legacy warehouse to modern lakehouse, reducing costs by 40%

// Overview

Led the migration of a healthcare organization's legacy data warehouse to a modern lakehouse architecture on Databricks. Unified disparate batch and streaming workloads while implementing strict HIPAA compliance and reducing infrastructure costs by 40%.

// The Challenge

  • Legacy Oracle warehouse costing $500K+ annually with poor scalability
  • Separate systems for batch analytics and real-time patient monitoring
  • Complex ETL processes taking 12+ hours to complete
  • HIPAA compliance requirements limiting cloud adoption options
  • Data scientists waiting days for data to be available for analysis

// Solution Delivered

  • Designed Delta Lake-based lakehouse architecture on Azure Databricks
  • Implemented automated data quality checks using Great Expectations
  • Built incremental ETL pipelines with Delta Lake change data capture
  • Created Unity Catalog governance framework for HIPAA compliance
  • Migrated 5TB of historical data with zero downtime
  • Established medallion architecture (bronze, silver, gold layers)
  • Trained internal team on lakehouse best practices and maintenance

// Impact Metrics

40%
Infrastructure cost reduction
5TB
Data migrated
85%
Faster query performance
3hrs
ETL runtime (down from 12hrs)

// Technology Stack

Databricks Delta Lake Apache Spark Azure Data Lake Azure Data Factory Unity Catalog Great Expectations Python SQL dbt Power BI
PROJECT_03

ML Pipeline Infrastructure for FinTech

Automated ML pipelines enabling 10x faster model deployment

// Overview

Designed and built an end-to-end machine learning pipeline infrastructure for a fintech company, automating feature engineering, model training, and deployment. The platform enabled data scientists to deploy models 10x faster while ensuring reproducibility and governance.

// The Challenge

  • Manual feature engineering taking weeks per model iteration
  • No standardized process for model deployment and monitoring
  • Lack of model versioning and experiment tracking
  • Data scientists spending 80% of time on infrastructure, not models
  • Regulatory requirements for model explainability and audit trails

// Solution Delivered

  • Built feature store using Feast for centralized feature management
  • Implemented Apache Airflow for automated pipeline orchestration
  • Created MLflow-based experiment tracking and model registry
  • Designed automated model validation and A/B testing framework
  • Built real-time and batch inference APIs with auto-scaling
  • Implemented comprehensive monitoring with drift detection
  • Established CI/CD pipelines for model deployment

// Impact Metrics

10x
Faster model deployment
100+
Features automated
15
Models in production
99.9%
Model API uptime

// Technology Stack

Apache Airflow MLflow Feast Google Cloud Platform BigQuery Vertex AI Python FastAPI Docker Kubernetes GitHub Actions Grafana