Principal Data Engineer

At Vomela our greatest asset is our people. As a full-service visual communications company, we are looking for creative and intellectual thinkers that work with our customers to create compelling brand solutions and foster meaningful connections. And while you're focused on creating big things for global and local brands, we will help you build a career you can be passionate about.

Apply now to find your place at Vomela.

Pay Range: $180 - 200k USD

Job Summary

The Principal Data Engineer is the highest-performing contributor on our data engineering team - the person who sets the technical bar, owns the data platform end to end, and delivers work that others study. You'll define and execute data strategy at the engineering level, operating as the technical point of the spear for how the organization builds, scales, and trusts its data. You write production code. You design the architecture. You solve the problems that block everyone else.

You mentor without being asked, influence without authority, and deliver without handholding. You're a force multiplier and you're hungry to shape not just the platform, but the broader data strategy of the business.

Microsoft Fabric is our data platform. This role is for someone genuinely energized by the Fabric ecosystem, who tracks its evolution closely and sees its breadth - Lakehouse’s, Event streams, Semantic models, Notebooks, Pipelines, Direct Lake as an opportunity, not a constraint. If you're looking for a role where your technical judgment shapes the trajectory of the entire data organization, this is exactly it.

What You'll Do...

• Design and implement dimensional models, star schemas, and snowflake schemas with rigor
• Build and maintain semantic models that serve as the single source of truth for business reporting
• Implement Slowly Changing Dimension (SCD) strategies appropriate to each domain
• Own master data engineering: golden record patterns, source-of-record authority, cross-system identity resolution
• Establish and enforce data modeling standards across the team

Design and operate real-time and near-real-time pipelines using streaming technologies (Kafka, Confluent Cloud, Fabric Eventstreams) — and know when streaming is the right answer and when it isn't

Relentlessly drive down data staleness in non-streaming scenarios through intelligent scheduling, incremental load optimization, and pipeline orchestration design

Own performance tuning across the full stack — query optimization, partition strategy, indexing, Delta table compaction, semantic model refresh efficiency, and Direct Lake readiness

• Apply operational engineering discipline: pipeline observability, alerting, SLA definition, failure recovery, and capacity planning
• Design and implement controls appropriate for sensitive data (financials, PII, HIPAA, etc.)
• ETL / ELT Pipeline Development
• Build robust, scalable, observable pipelines — watermark-based incremental loads, CDC patterns, batch and streaming architectures
• Ensure pipelines are idempotent, recoverable, and production-hardened
• Serve as the senior technical voice in code review — your approval carries weight
• Report & Analytics Delivery
• Translate business requirements into semantic models and report-layer artifacts that non-technical users can trust and navigate

Serve as the platform's primary technical interface across consumer groups: Power BI report builders needing trusted, well-modeled semantic layers; AI/ML developers needing governed, feature-ready data surfaces; application developers consuming data via SQL endpoints, REST APIs, or Direct Lake

Define and enforce data contracts — schema stability, access patterns, SLAs — for each consumer class

Own the developer experience of the platform: discoverability, documentation, and onboarding

Requirements

• Required
• Microsoft Fabric: Lakehouses, Notebooks, Dataflows Gen2, Event streams, Semantic Models, Direct Lake mode
• Power BI: report development, dataset/semantic model design, DAX proficiency
• SQL Server / Azure SQL/Postgres: query optimization, schema design, stored procedures
• Azure DevOps: Git-based development workflows, CI/CD for data pipelines
• · Demonstrated use of AI coding assistants in a production engineering workflow
• · Ability to critically evaluate, edit, and improve AI-generated code and artifacts
• · Clear understanding of where AI accelerates work and where it introduces risk

Preferred Qualifications

• Familiarity with broader Azure Data Services (Azure Data Factory, Synapse Analytics, ADLS Gen2, Event Hubs) as complementary tooling
• Experience in a private equity-backed or multi-entity portfolio company environment
• Exposure to MDM platforms (Profisee, Semarchy, Ataccama, or equivalent)
• Experience with Confluent Cloud / Apache Kafka for streaming ingestion into Fabric or Synapse
• Familiarity with cross-tenant Azure / Fabric architecture
• Background in business analysis, solutions architecture, or pre-sales engineering
• Microsoft Fabric or Azure Data Engineer certifications