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 ar