[Hiring] Data Platform & Systems Engineer @Stellar Virtual

Role Description

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.

• 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.
• 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.
• 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.
• 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.
• 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.
• 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.
• 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.
• 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.
• 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.
• 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.
• 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.

Qualifications

• 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.
• 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.

Requirements

• 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.

Benefits

• 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.