Data Platform & Systems Engineer (27784925)

Page Mechanical Group, Inc.; Washington (MO); On-site; USD 110,000 - 140,000; Full time

Benefits offered by this job

Competitive vacation & PTO

Comprehensive health plans

401(k) retirement plan

FSA/HSA

Company holidays

Job summary

Stellar Virtual is seeking a Data Platform & Systems Engineer to own the data platform, ingestion pipelines, data warehouse, and delivery systems across the full stack (Python, Azure, SQL, dbt). You will drive reliability, scalability, and observability, applying AI-assisted development and automation while collaborating with teams to shape the platform roadmap and operational excellence.

This role emphasizes ownership, clear communication, and the ability to work independently as Stellar

Qualifications

• 3+ years in data/engineering or related role; remote environment preferred.
• Strong Python and SQL proficiency.
• Experience with dbt, Azure and orchestration tooling.

Responsibilities

• Build and maintain Python-based ingestion pipelines with APIs, SFTP, files and external sources.
• Develop, optimize SQL and dbt models across staging to analytics layers.
• Build orchestration across ingestion, transformation, and delivery workflows.
• Design monitoring, logging, and observability for pipelines.
• Implement data quality checks and automated tests across pipelines.
• Collaborate with stakeholders to translate requirements into data products.
• Support deployment, CI/CD, and incident resolutions.

Skills

Python

SQL

dbt

Azure

Git & CI/CD

Cloud data platforms

APIs & data integrations

Google Workspace

Data engineering

Communication

Education

Bachelor's degree in CS/Data Science/Engineering

Equivalent professional experience

High school diploma or GED

Tools

dbt

Azure Data Factory (ADF)

Git

Job description

Position Details

• Reporting Manager: Director, Digital Learning Systems
• Status: Salary, Exempt
• Type: Regular, Full-Time (12 Months)
• Program: Stellar Virtual OMG - Corp

Overview

The Data Platform & Systems Engineer will be responsible for developing, maintaining, and progressively assuming day‑to‑day ownership of Stellar Virtual's data platform, including its ingestion pipelines, data warehouse, transformation layer, orchestration, integrations, and delivery systems. The engineer works across the full stack—Python, Azure, SQL, dbt, orchestration, and downstream delivery to ensure the platform is reliable, scalable, maintainable, and operationally sound. Effective use of AI‑assisted development and automation is an essential part of the role, both to accelerate engineering work and to improve system monitoring, management, and reliability. As familiarity with the platform grows, the engineer will be expected to work increasingly independently, identify opportunities for improvement, and contribute meaningfully to technical architecture and the platform roadmap. Success in this role requires strong engineering judgment, ownership, clear communication, and consistent follow‑through, in support of Stellar Virtual's mission of empowering families and unleashing potential.

Essential Duties

• Data Ingestion
  • Build, maintain, and improve Python-based ingestion pipelines integrating APIs, SFTP sources, files, and other external systems.
  • Design pipelines using reliable engineering patterns, including incremental processing, idempotency, retry and backoff handling, checkpointing, and recoverability.
  • Diagnose and resolve source-system, authentication, schema, and data‑transfer issues.
  • Develop reusable ingestion patterns and shared components to reduce duplication and improve maintainability across integrations
• Data Modeling & Transformation
  • Develop, maintain, and optimize SQL and dbt models across staging, intermediate, and analytical layers.
  • Translate source-system data into well-defined, reusable data models that support reporting, analytics, operational workflows, and downstream applications.
  • Evaluate model performance, query efficiency, materialization strategies, and data volume as the platform scales.
  • Maintain clear lineage and consistent modeling conventions across the warehouse.
• Data Modeling & Transformation
  • Develop, maintain, and optimize SQL and dbt models across staging, intermediate, and analytical layers.
  • Translate source-system data into well-defined, reusable data models that support reporting, analytics, operational workflows, and downstream applications.
  • Evaluate model performance, query efficiency, materialization strategies, and data volume as the platform scales.
  • Maintain clear lineage and consistent modeling conventions across the warehouse.
• Orchestration & Pipeline Operations
  • Build and maintain orchestration across ingestion, data movement, transformation, validation, and delivery workflows.
  • Manage dependencies, scheduling, retries, failure handling, and recovery across multi-stage pipelines.
  • Troubleshoot failed or degraded production jobs and perform root‑cause analysis.
  • Improve pipeline reliability, runtime, scalability, and operational simplicity over time.
  • Develop automation that reduces manual intervention and supports reliable day‑to‑day operation of the platform.
• Data Quality, Testing & Validation
  • Design and implement data‑quality checks across all pipeline stages: ingestion to Blob, Blob to SQL, SQL to dbt models, and dbt to downstream outputs.
  • Build and maintain dbt tests covering nullability, uniqueness, referential integrity, accepted values, freshness, and business‑specific validation rules.
  • Implement reconciliation checks across source systems, warehouse datasets, and downstream outputs.
  • Define appropriate thresholds for failures, warnings, and anomalies and ensure they are consistently enforced.
  • Ensure new pipelines and models include appropriate automated test coverage by default.
  • Investigate data‑quality failures and address underlying causes rather than relying on recurring manual corrections.
• Monitoring, Alerting & Observability
  • Implement structured logging across ingestion, transformation, orchestration, and delivery layers.
  • Establish run‑level visibility including run IDs, row counts, durations, status, errors, and other relevant operational metadata.
  • Configure monitoring and alerts for pipeline failures, missing or late data, unexpected volume changes, data anomalies, long runtimes, and repeated retries.
  • Ensure failures are visible, actionable, and traceable through the system.
  • Develop operational metrics and tools that make platform health understandable without requiring manual inspection of individual jobs.
• Data Delivery & Systems Integration
  • Build and maintain reliable outbound data pipelines and integrations supporting internal systems, vendors, reporting platforms, and other downstream consumers.
  • Develop API‑, file‑, database‑, and event‑based integrations as required.
  • Ensure outbound processes include appropriate validation, logging, retry handling, and reconciliation.
  • Work with stakeholders to translate operational requirements into maintainable technical interfaces and data products.
• Deployment & Engineering Practices
  • Use Git‑based development workflows, code review, testing, and CI/CD practices to manage changes safely across environments.
  • Support automated deployment and environment promotion for data pipelines, dbt models, orchestration, and supporting services.
  • Maintain appropriate separation between development, staging, and production environments.
  • Improve engineering standards, reusable patterns, and tooling as the platform matures.
  • Participate in incident resolution and implement improvements that reduce recurrence.
• AI & Automation
  • Use AI‑assisted development tools effectively to accelerate software development, debugging, testing, documentation, and analysis while maintaining appropriate engineering review and validation.
  • Identify opportunities to automate repetitive operational, monitoring, troubleshooting, and maintenance activities.
  • Develop AI‑enabled tools or agents where they provide meaningful improvements to platform reliability, developer productivity, or business operations.
  • Evaluate emerging tools and approaches and recommend adoption where they materially improve the platform.
• Documentation & Knowledge Transfer
  • Maintain technical documentation covering architecture, pipelines, integrations, operational procedures, dependencies, and system behavior.
  • Document new systems and material changes sufficiently for another engineer to understand, operate, and troubleshoot them.
  • Develop runbooks for common operational failures and recovery procedures.
  • Share knowledge with technical and business stakeholders and reduce unnecessary dependence on undocumented institutional knowledge.
• Architecture & Continuous Improvement
  • Develop a strong understanding of the end‑to‑end data platform and progressively assume ownership of its day‑to‑day development and operation.
  • Identify technical debt, reliability risks, scalability constraints, and opportunities for architectural improvement.
  • Contribute meaningfully to platform architecture, technical standards, tooling decisions, and the engineering roadmap.
  • Balance immediate business needs with maintainability, reliability, security, and long‑term platform health.
  • Work independently on ambiguous technical problems, escalating appropriately while bringing proposed solutions rather than problems alone.
• Collaboration & Business Support
  • Communicate technical issues, risks, tradeoffs, and progress clearly to technical and non-technical stakeholders.
  • Translate business requirements into practical, maintainable technical solutions.
  • Demonstrate consistent ownership and follow‑through from problem identification through implementation and validation.
  • Mission Focus: You will play a key role in our collective success by proactively pivoting to support emerging business needs and strategic priorities directed by your manager.

Knowledge, Skills & Attributes

• Technical Experience
  • 3+ years of experience in data engineering, systems engineering, software engineering, or a related technical role; experience working in a remote environment is preferred.
  • Strong proficiency in Python and SQL.
  • Hands‑on experience with dbt and Azure Data Factory (ADF) or comparable data transformation, orchestration, and ETL/ELT tooling.
  • Experience with Git‑based development workflows, including branching, code review, and CI/CD.
  • Experience working with cloud data platforms and services, preferably Microsoft Azure or a comparable cloud environment.
  • Familiarity with APIs, file‑based integrations, SFTP, relational databases, and common data interchange formats. dv c
  • Minimum of 1–2 years of experience using the Google Workspace suite (Docs, Sheets, Slides, Drive, Workspace) in a professional setting.
  • Demonstrated ability to communicate complex ideas via written channels with high clarity and low ambiguity.
  • Access to a private, reliable, and secure high-speed internet connection with a minimum of 25 Mbps download and 10 Mbps upload speeds.

• Professional Skills & Working Style
  • Demonstrated ability to work independently, prioritize competing responsibilities, and consistently drive work through completion with limited supervision.
  • Strong troubleshooting and engineering judgment, including the ability to diagnose issues across multiple systems, identify root causes, and work effectively through ambiguous technical problems.
  • Strong written communication skills, with the ability to explain technical concepts, decisions, risks, and status clearly to technical and non-technical stakeholders.
  • Comfortable using AI‑assisted development tools to accelerate coding, debugging, testing, documentation, analysis, and operational automation while appropriately validating outputs.
  • Ability to identify opportunities for automation, standardization, and continuous improvement.

• Success Profile
  • Success in this role will be evaluated across the following areas:
  • Data Platform Engineering: Builds and maintains reliable, maintainable, and well‑tested pipelines, models, integrations, and supporting systems across the data platform.
  • Data Reliability & Quality: Implements effe2ctive testing, monitoring, observability, and alerting so that data and system failures are detected quickly, understood, and resolved.
  • Operational Ownership: Develops a strong understanding of the end‑to‑end platform and progressively assumes responsibility for its day‑to‑day operation, troubleshooting, and improvement.
  • Engineering Judgment: Makes sound technical decisions, recognizes tradeoffs, addresses root causes, and balances immediate business needs with long‑term platform health.
  • Independence & Follow‑Through: Works effectively with limited supervision, manages priorities, communicates progress and risks clearly, and reliably drives work to completion.
  • Architecture & Continuous Improvement: Identifies technical debt, reliability risks, and opportunities for automation or architectural improvement and contributes meaningfully to the platform roadmap.
  • AI & Automation: Uses AI effectively and responsibly to increase engineering productivity

Education & Certification Requirements

• A high school diploma or equivalent (GED) is required for all positions.
• Bachelor's degree in Computer Science, Data Science, Mathematics, Engineering, or another related technical/STEM field preferred.
• Equivalent professional experience may be considered in lieu of a bachelor's degree.

Benefits & Incentives (Full-Time Only)

• Competitive vacation and paid time off plans
• Comprehensive medical (HMO, HDHP, and PPO), dental, and vision plans with varying coverage for employees and their families, with options available in all states, including a high-deductible health plan (HDHP) option.
• 401(k) retirement plan (pre‑and post‑tax options) with matching contribution up to 3% after one year of employment
• Additional supplemental disability, life, critical illness, hospital indemnity, accident, legal aid, and ID theft protection plans
• Flexible Spending Account (FSA) or Health Savings Account (HSA)
• Two paid closure weeks each year (weeks of July 4 and December 25)
• 11 Company‑paid Holidays

State Hiring Eligibility

Primary States: Stellar Virtual actively hires educators in Arizona (AZ), Indiana (IN), Iowa (IA), Missouri (MO), Oregon (OR), Tennessee (TN), Texas (TX), and Washington (WA).

Limited States: Stellar Virtual may consider candidates residing in Alabama (AL), Arkansas (AR), Colorado (CO), Florida (FL), Georgia (GA), Illinois (IL), Kansas (KS), Kentucky (KY), Louisiana (LA), Massachusetts (MA), Mississippi (MS), New Hampshire (NH), North Carolina (NC), Oklahoma (OK), Pennsylvania (PA), South Carolina (SC), Utah (UT), Virginia (VA), and Wisconsin (WI).

State Eligibility Requirements: Stellar Virtual is currently unable to hire candidates who reside in the following states: Alaska, California, Connecticut, Delaware, Hawaii, Maine, Maryland, Michigan, New York, and Rhode Island, or any state not otherwise listed as an approved hiring location for this organization. Applicants residing in any of these states, or in any state not included in Stellar Virtual's approved hiring locations, will not be considered for this position. We encourage candidates to confirm their state of residence aligns with our current hiring footprint before applying.

Employment "At-Will" Statement

For W-2 employment, Stellar Virtual is an at‑will employer. This means the employee and Stellar Virtual can terminate the employment at any time, with or without cause or notice, as legally permitted. This job description does not guarantee continued employment or a contract. All 1099 status is excluded.

Equal Employment Opportunity (EEO)

Stellar Virtual is an equal opportunity employer committed to diversity and inclusion. All qualified applicants are considered for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, or veteran status.