Data Engineer/Data Architect

Vaco Recruiter Services; Town of Florida (NY); On-site; USD 120,000 - 160,000; Full time

Job summary

Unknown is seeking a Senior Data Engineer / Data Architect in Fort Lauderdale, FL, for hands-on development and leadership of our enterprise data foundation. This role connects ERP, CRM, ecommerce, marketing, production, finance, and internal systems to enable trusted reporting, AI initiatives, automation, and internal applications.

The candidate must design scalable architectures, set technical standards, and personally build production-grade data services from the ground up.

Qualifications

• Hands-on experience building production-grade data platforms.
• Expertise designing enterprise data platforms and governing data models.
• Ability to build and ship data pipelines from scratch across systems.

Responsibilities

• Design and implement scalable data architecture for reporting, AI, and internal apps.
• Build production-grade data pipelines and lakehouse solutions in Databricks.
• Develop ETL/ELT processes using SQL, Python, Spark, and orchestration tools.
• Create secure APIs and data services for reporting and automation.
• Establish data governance, standards, and release pipelines for data work.
• Collaborate with cross-functional teams to translate data risks into business actions.

Skills

Databricks

Data architecture

Hands-on engineering

Tools

Apache Spark

ETL tools

APIs

Job description

Senior Data Engineer / Data Architect

Location: Fort Lauderdale, FL

Work Arrangement: Fully onsite

Employment Type: Direct hire, full-time

Role Type: Hands-on individual contributor and technical lead

Position Overview

We are seeking a Senior Data Engineer / Data Architect to lead the design and hands‑on development of our enterprise data foundation. This role will connect data across ERP, CRM, ecommerce, marketing, production, finance, fulfillment, and internal business systems to support trusted reporting, AI initiatives, automation, and internal applications.

This is not an architecture‑only or advisory position. The successful candidate must have genuine architecture experience and the ability to personally build production‑grade data platforms, pipelines, models, integrations, quality controls, and data services from the ground up.

The ideal candidate combines strong engineering execution with the ability to define scalable architecture, establish technical standards, and communicate effectively with both technical and business stakeholders.

Top Requirements

1. Hands‑on experience with Databricks, including Lakehouse architecture, Apache Spark, data pipelines, notebooks, workflows, and production deployments.
2. True data architecture experience, including designing enterprise data platforms, defining systems of record, developing data models, establishing data governance, and making cross‑system architecture decisions.
3. Demonstrated success engineering implementations from the ground up, rather than only maintaining or advising on existing solutions.

Key Responsibilities

Data Architecture

• Assess the current data environment, including systems, databases, APIs, integrations, reports, scheduled jobs, and data owners.
• Design and implement a scalable enterprise data architecture supporting reporting, AI, analytics, and internal applications.
• Define authoritative systems of record for customer, product, order, revenue, inventory, location, marketing, and production data.
• Establish common data models, identifiers, data contracts, schema standards, retention policies, and integration patterns.
• Determine appropriate use of batch processing, real‑time events, APIs, webhooks, and governed data services.
• Develop architecture diagrams, technical standards, roadmaps, and implementation plans.

Data Engineering and Implementation

• Build production‑grade data pipelines and lakehouse solutions in Databricks.
• Develop reliable ETL and ELT processes using SQL, Python, Apache Spark, and orchestration tools.
• Integrate data from ERP, CRM, ecommerce, marketing, manufacturing, finance, and internal applications.
• Create tested transformations that produce consistent and reusable business data.
• Build secure APIs and data services for approved reporting, AI, automation, and application use cases.
• Design systems that handle failures, changing schemas, retries, late‑arriving data, and duplicate‑processing risks.
• Establish source control, automated testing, code review, deployment pipelines, and release processes for data engineering work.

Data Quality and Reliability

• Implement automated checks for completeness, accuracy, freshness, duplication, volume changes, and consistency.
• Reconcile key business measures such as orders, revenue, inventory, customer counts, and production activity across systems.
• Monitor pipeline performance, failed jobs, delayed data, schema changes, and data‑quality incidents.
• Build alerting, operational dashboards, runbooks, and incident‑response processes.
• Work with source‑system owners to correct the root causes of data issues.

Governance, Security, and Documentation

• Establish practical standards for data ownership, classification, access, retention, and approved use.
• Apply role‑based access controls, encryption, audit logging, and appropriate protection for sensitive and confidential information.
• Maintain clear data definitions, lineage, integration documentation, runbooks, and architecture diagrams.
• Partner with IT, Legal, Infrastructure, Applications, and business leaders on privacy, security, and compliance requirements.
• Create governed access patterns that reduce uncontrolled direct access to production systems.

Reporting, AI, and Internal Applications

• Develop trusted, reusable data models for reporting, dashboards, forecasting, and business analysis.
• Prepare structured and governed data for AI agents, retrieval systems, automations, machine learning, and internal applications.
• Partner with the Director of AI and internal product teams to accelerate delivery of data and AI use cases.
• Establish standards for monitoring how applications and AI solutions access and use company data.

Cross‑Functional Leadership

• Collaborate with technology, finance, operations, ecommerce, marketing, sales, production, and other business teams.
• Translate technical data risks into clear business impacts, options, costs, and recommended actions.
• Review vendor‑built integrations for documentation, security, maintainability, and quality.
• Provide technical leadership and establish standards that improve how teams collect, define, share, and use data.
• Work independently, set priorities, document decisions, and deliver measurable results.