Engineering Leadership · Data · AI

Michael Leuer

R&D Manager | Big Data, AI & Scalable Systems

Building data-driven platforms, leading technical initiatives, and turning complex problems into practical solutions.

About

I lead R&D efforts at the intersection of big data, AI, and cloud-scale analytics. My day-to-day spans architecture decisions, hands-on implementation, and coordinating engineering teams to deliver systems that actually work at scale. I care about building things that are maintainable, performant, and grounded in real-world requirements — not just technically impressive on paper.

My background is in distributed systems and data engineering, but I've spent the last several years moving between individual contribution and technical leadership. I enjoy the challenge of translating messy, ambiguous problems into well-structured systems — whether that means designing a new data pipeline, improving an orchestration workflow, or helping a team ship a critical project on time.

I believe the best engineering leaders stay close to the code. I'm most effective when I can bridge the gap between strategic goals and technical execution, working across teams to align architecture with business outcomes.

Focus

Big Data & AI Systems

Role

R&D Manager

Approach

Builder & Leader

Impact

Production Scale

Impact & Experience

Outcomes and contributions across data engineering, technical leadership, and systems architecture.

TB-scale daily throughput

Data Pipeline Architecture

Designed and built cloud-scale data pipelines processing terabytes of data daily, enabling near real-time analytics across distributed systems.

Impact: Reduced data latency from hours to minutes, enabling faster decision-making across the organization.

Multi-team coordination

Technical Team Leadership

Led cross-functional engineering teams through complex technical initiatives, from initial architecture through production deployment and beyond.

Impact: Delivered critical platform milestones on schedule while improving team velocity and code quality.

Hundreds of automated workflows

Workflow Automation & Orchestration

Built and optimized job orchestration systems that coordinate hundreds of interdependent data workflows, reducing manual intervention and failure rates.

Impact: Cut manual operations effort significantly and improved pipeline reliability to 99%+ success rates.

Organization-wide adoption

Data-Driven Decision Systems

Created analytics platforms and reporting tools that translate raw data into actionable business insights for stakeholders at every level.

Impact: Enabled data-driven decisions that directly influenced product strategy and resource allocation.

Department-wide adoption

Engineering Process Improvement

Introduced CI/CD practices, code review workflows, and engineering standards that improved development velocity and reduced production incidents.

Impact: Measurably reduced deployment failures and shortened release cycles.

Multi-stakeholder projects

Cross-Team Solution Delivery

Worked across engineering, product, and operations teams to define requirements, resolve technical blockers, and deliver integrated solutions.

Impact: Consistently bridged gaps between technical execution and business goals, ensuring projects delivered real value.

Projects

Selected projects spanning data platforms, AI systems, and engineering tooling.

Cloud Analytics Platform

End-to-end analytics platform built on Azure that ingests, transforms, and serves data from multiple sources for real-time and batch reporting use cases.

AzureApache SparkPythonSQLDelta Lake

Problem

Fragmented data sources and slow, manual reporting processes prevented teams from accessing timely insights.

Outcome

Unified data from 12+ sources into a single platform, reducing report generation time from days to minutes and enabling self-service analytics for 200+ users.

Workflow Orchestration Engine

Custom orchestration system built on Apache Airflow to manage complex, interdependent data workflows across multiple environments with robust monitoring and alerting.

Apache AirflowPythonDockerREST APIsPostgreSQL

Problem

Manual job scheduling and lack of dependency management caused frequent pipeline failures and required constant human intervention.

Outcome

Automated 300+ workflows with dependency-aware scheduling, reducing pipeline failures by 85% and eliminating most manual operational overhead.

AI-Powered Data Quality System

Machine learning system that continuously monitors data quality across pipelines, detecting anomalies, schema drift, and data distribution shifts before they impact downstream consumers.

Pythonscikit-learnPandasAzure FunctionsSQL

Problem

Data quality issues were discovered late — often by end users — causing trust erosion and costly manual investigations.

Outcome

Caught 90%+ of data quality issues before they reached production dashboards, saving significant investigation time per incident.

Executive Reporting Dashboard

Interactive reporting dashboard for executive stakeholders providing real-time KPIs, trend analysis, and drill-down capabilities across business-critical metrics.

ReactD3.jsTypeScriptREST APIsSQL

Problem

Executive teams relied on static, weekly reports that were often outdated by the time decisions were made.

Outcome

Delivered a self-service dashboard adopted by C-level stakeholders, enabling real-time visibility into key metrics and faster strategic decisions.

Internal Developer Tooling

Suite of CLI tools and CI/CD integrations that standardized development workflows, automated testing, and streamlined deployment processes for the data engineering team.

PythonDockerGitHub ActionsBashAzure DevOps

Problem

Inconsistent development environments and manual deployment steps slowed delivery and introduced frequent configuration-related bugs.

Outcome

Standardized development workflows across the team, cutting deployment time by 70% and virtually eliminating environment-related issues.

Documentation & Technical Writing

Engineering write-ups, architecture overviews, and lessons learned from building data systems at scale.

September 15, 2025

Designing a Cloud-Scale Analytics Pipeline

A deep dive into the architecture decisions behind building a data pipeline that processes terabytes daily on Azure, from ingestion to serving.

Data PipelinesCloudArchitectureAzure
Read article

July 20, 2025

Lessons Learned: Migrating to Event-Driven Architecture

Practical insights from moving a batch-heavy data platform to an event-driven model — what worked, what didn't, and what we'd do differently.

ArchitectureEvent-DrivenDistributed Systems
Read article

May 10, 2025

Workflow Orchestration at Scale with Airflow

How we tamed 300+ data workflows with Apache Airflow — covering DAG design patterns, failure handling, and operational practices that actually scale.

Data PipelinesAirflowAutomationDevOps
Read article

March 8, 2025

Building AI-Powered Data Quality Checks

How we used machine learning to detect data quality issues before they reached production dashboards — architecture, model choices, and operational results.

AIData QualityMachine LearningData Pipelines
Read article

January 22, 2025

Architecture Patterns for Multi-Tenant Data Platforms

Practical patterns for building data platforms that serve multiple teams and use cases without turning into an unmaintainable monolith.

ArchitectureCloudData PlatformsMulti-Tenant
Read article

Skills & Expertise

Languages

PythonC#TypeScriptSQLBash

Cloud & Infrastructure

AzureDockerKubernetesTerraformCI/CD Pipelines

Data & Analytics

Apache SparkPandasDelta LakeData ModelingETL / ELT PipelinesSQL Databases

AI & Machine Learning

scikit-learnData Quality MLAnomaly DetectionNLP Fundamentals

Backend & Systems

REST APIsDistributed SystemsApache AirflowEvent-Driven ArchitectureMicroservices

Tools & Platforms

GitGitHub ActionsAzure DevOpsJiraConfluenceVS Code

Get in Touch

Open to discussing data systems, AI, and engineering challenges. Whether it's architecture, team building, or hard technical problems — I'm always happy to connect.

michael@mikes3ds.comLinkedInGitHub