Job Summary

The Data Engineer will own the full lifecycle of a modern data lake and data warehouse environment—architecture, ingestion, transformation, and analytics enablement. This role is responsible for building robust data pipelines, staging operational data for reporting, and supporting the organization’s transition away from consultant-heavy models. The ideal candidate is a hands-on builder who can operate in a fast-paced operational setting and manage both engineering and light architectural responsibilities.

Key Responsibilities

Data Lake Ownership & Architecture

• Design and maintain the data lake / lakehouse ecosystem, including raw (bronze), transformed (silver), and curated (gold) layers.

• Participate in architectural decisions for the core platform (e.g., choosing between cloud-native tools like Fabric vs. open-source streaming ecosystems such as Kafka).

• Establish standards for data ingestion, transformation, and storage to support long-term scalability.

ETL / Data Pipeline Development

• Build and maintain ETL/ELT pipelines from operational systems, EDI feeds, APIs, and external customer data sources.

• Model and populate relational database tables that support reporting, analytics, and KPIs.

• Use orchestration tools (e.g., Airflow or similar) to schedule and monitor workflows.

Analytics & BI Enablement

• Prepare curated datasets optimized for dashboarding and analytics (Power BI or similar).

• Work with business and technical teams to deliver metrics, operational reporting, and KPI visibility.

• Ensure data quality, consistency, and accessibility across environments.

Operational Data Integration

• Ingest operational data from systems such as warehouse, supply-chain, order-management, or other logistics-style applications.

• Map inbound data into standardized internal schemas and ensure accurate persistence into core databases.

• Reduce reliance on external consultants by building strong internal data capabilities.

Cross-Functional Collaboration

• Partner with IT leadership, engineering, and operations to understand data requirements and design scalable solutions.

• Communicate effectively with both technical and non-technical stakeholders.

• Share knowledge and help strengthen overall data maturity across the team.

Required Qualifications

• Strong experience as a Data Engineer with hands-on responsibility for building data lakes, warehouses, and production-grade data pipelines.

• Proficiency in SQL, relational modeling, stored procedures, and optimizing ETL performance.

• Experience with at least one modern data platform (e.g., Microsoft Fabric or Kafka/open-source streaming stack) and willingness to work across technologies.

• Background supporting operational data environments such as warehousing, logistics, supply chain, manufacturing, or other high-change environments.

• Ability to manage complex ingestion flows from EDI, API-based integrations, and transactional systems.

• Comfortable taking ownership in a small, fast-moving team where individuals wear multiple hats.

• Strong discipline around change control, documentation, testing, and deployment hygiene.

Preferred Qualifications

• Experience evaluating tradeoffs between cloud-native lakehouse platforms and self-hosted streaming/ingestion architectures.

• Experience with Airflow or similar orchestration tools.

• Exposure to operational applications such as warehouse management systems (WMS) or ERP data extraction.

• Prior work reducing consultant dependency and building internal engineering capability.

• Experience in environments with rapid change cycles, continuous deployments, or operational time-sensitive data requirements.

Ideal Candidate Profile

• Hands-on builder who enjoys owning the full pipeline from ingestion → transformation → analytics.

• Thrives in high-adrenaline operational environments where priorities shift quickly.

• Balances engineering strength with enough architectural awareness to guide long-term decisions.

• Curious, proactive, and process-oriented — someone who wants responsibility, not micromanagement.

• Clear communicator who collaborates well across IT, business operations, and leadership.