Big Data Engineer - Nashville, TN - Hybrid Preferred / Remote accepted

Stellent IT LLC
Nashville, TN, United States
about 2 months ago

Role details

Contract type
Temporary to permanent
Employment type
Full-time (> 32 hours)
Experience level
Expert
Working hours
Regular working hours
Job source

Tech stack

Agile Methodology Artificial Intelligence Airflow Amazon Web Services Microsoft Azure Big Data Cloud Computing Databases Extract Transform Load (ETL) Data Systems Hadoop Distributed File System Python (Programming Language)
+14 more
PostgreSQL Microsoft SQL Server Software Engineering Software Systems Unstructured Data Build Management Kubernetes Information Technology Health Level Seven International Azure AKS Data Management Machine Learning Operations Tools for Reporting Docker

Job description

  1. Cloud Technology/HDFS big data ecosystem experience - bringing data sources into GCP, transforming and loading to databases; ETL at scale.
  2. Microsoft SQL or Postgres (advanced features) - strong development experience required, not just querying familiarity. Nice to have:
  • Kubernetes exp -Dagster

  • It’s replacing their current pipeline orchestration setup
  • It runs on Azure Kubernetes Service (AKS), so Docker/Kubernetes experience pairs well with it
  • It’s a newer, niche platform - he doesn’t expect candidates to have it, but said if you come across someone with Dagster experience, “that’d be somebody I’d be interested in meeting with”
  • Not a hard requirement, but a significant differentiator

-

  • The Data Management team needs a senior-level engineer who can operate independently, design and build GCP-based data solutions, and mentor junior developers - all within a fast-paced, matrixed Agile environment. The team is scaling its enterprise data capability and needs someone who can own technically complex work end-to-end with minimal supervision.

Who is the internal customer that this role is ultimately supporting:

  • Data scientists, business analysts, and IT and business leaders across the enterprise who rely on structured, semi-structured, and unstructured data pipelines for analysis, reporting, and AI/ML use cases.

Differentiators for the opportunity (“sizzle”):

  1. Fully remote if need be. Onsite preferred. Note* if remote, no chance of conversion.
  2. High-visibility enterprise role - this person sets technical direction for a group of applications and shapes the GCP data architecture across the organization.
  3. AI/ML integration scope - not just plumbing data; this role analyzes business requirements and designs AI/ML-based solutions, giving strong engineers a path to meaningful, cutting-edge work. Job Descripti Overview: Responsibilities: Build and support a GCP-based ecosystem for enterprise-wide analysis of structured, semi-structured, and unstructured data Bring new data sources into GCP/HDFS, transform and load to databases Design, develop, deploy, and support software systems with minimal supervision Analyze requirements and design AI/ML-based solutions; integrate those solutions for customer environments Support regular data movement between clusters; manage production support SLAs Collaborate with data scientists, business analysts, and IT/business leaders to understand data needs and use cases Lead and mentor junior developers; take responsibility for technically robust, end-to-end solutions Work within Agile practices and principles across a mixed consultant/employee team

Requirements

Bachelor’s degree in Computer Science, Software Engineering, or related field Production-level Python development experience Strong experience with ETL processes or analytics/reporting applications Experience with Microsoft Azure or AWS (GCP is the primary platform in use) Advanced Microsoft SQL or Postgres development experience Solid expertise developing modern, scalable applications Preferred: GCP / HDFS big data ecosystem experience (active platform - strong differentiator) AI/ML solution design and integration HL7 or other healthcare integration experience Supply chain or healthcare industry background

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