Junior Software/Data Engineer - (Remote)
5 days ago⚠️ Before applying, please carefully review all the job requirements
- Location:
- Remote (United States, United Kingdom, Canada, Australia)
- Work Arrangement:
- Remote for Any where
- Experience:
- 1–3 years (internships, academic, or personal projects count)
- Job Type:
- Full Time
- Company:
- Kreate
- Salary:
- 95,000 - 105,000 USD per year
- Posted:
- 2026-09-28
- Reporting To:
- VP of AI & Analytics
Key Responsibilities
- Develop, configure, and maintain data ingestion pipelines and processing notebooks within Microsoft Fabric using Data Factory, Python/PySpark, and Delta tables.
- Conduct daily operational monitoring of scheduled pipeline runs and container jobs, triaging execution failures and ensuring partially completed jobs reflect proper failure status.
- Implement and manage automated data quality validation checks covering record counts, data freshness, duplicate detection, and source system reconciliation with automated alert triggers.
- Transition standalone desktop scripts and ad-hoc code into version-controlled, monitored, and managed production infrastructure.
- Structure new raw source tables into standardized Silver dimensional models and Gold fact tables with defined data grain and business metrics.
- Maintain runbooks, system documentation, and the centralized data catalog to keep pipeline logic and data architectures accessible and maintainable.
- Collaborate with senior engineering staff to investigate pipeline bugs, perform root cause analysis, and engineer reliable infrastructure fixes.
- Partner with internal data, analytics, and business units to verify data integrity and ensure datasets are properly structured for downstream reporting.
Required Skills and Qualifications
- ★ 1–3 years of professional experience in data engineering, analytics engineering, software development, or a related discipline (substantial internships, academic assignments, or personal projects count toward experience).
- ★ Strong SQL proficiency, including multi-table joins, window functions, and an understanding of fan-out row duplication during join operations.
- ★ Working knowledge of Python, including hands-on experience with pandas or PySpark libraries.
- ★ Practical daily experience using Git for source code version control and collaborative workflows.
- ★ Meticulous attention to detail and analytical patience when auditing existing data pipelines to determine metric variances.
- ★ Strong commitment to data accuracy, quality control, and validation, with the judgment to flag discrepancies rather than deliver unverified metrics.
- ★ Analytical problem-solving capabilities with a commitment to mastering modern data engineering frameworks, tools, and methodologies.
Preferred Skills and Experience
- ★ Hands-on exposure to Microsoft Fabric, Azure Data Factory, Databricks, or Azure Synapse Analytics platforms.
- ★ Familiarity with Delta Lake storage formats and Parquet file architectures.
- ★ Foundational understanding of Microsoft Azure cloud services, including Azure App Service, Container Apps, Key Vault, and Entra ID.
- ★ Prior exposure to manufacturing or ERP datasets, such as Oracle, IQMS/DELMIAworks, SAP, or Epicor platforms.
- ★ Hands-on experience working with Power BI for analytics reporting.
Who May Be a Good Fit for This Role?
- This role may be suitable for candidates with 1 to 3 years of foundational experience in data engineering, analytics development, or software engineering who want to work remotely on modern cloud data infrastructure. It is well suited for early-career professionals or recent graduates with strong SQL, Python, and Git capabilities who have gained practical experience through internships, academic coursework, or personal engineering repositories. Candidates who possess thorough attention to detail, enjoy investigating complex pipeline behaviors, and take pride in rigorous data quality checks will find this opportunity with Kreate well matched to their career trajectory.
What the Company Offers
- ★ Competitive remote compensation package ($95,000–$105,000 USD per year).
- ★ Direct mentorship from senior data engineering leadership and executive exposure to the VP of AI & Analytics.
- ★ Hands-on practical experience working with modern cloud lakehouse architectures and Microsoft Fabric environments.
Equal Opportunity & Employment Terms
- Kreate is an Equal Opportunity Employer. Statements in this job posting reflect the general nature and operational level of work performed and do not constitute an exhaustive list of all duties, skills, or responsibilities. Furthermore, this job description does not establish a contract for employment and remains subject to administrative updates at the discretion of the company.
Prepare for Your Interview
- These suggested questions can help you prepare for an interview by focusing on the skills, responsibilities, and technical areas relevant to this role.
Suggested Interview Questions
- Q: How do you write SQL window functions to handle deduplication or calculate running totals across dynamic datasets?
- Q: Can you explain how a SQL join can cause row fan-out, and what steps do you take to detect and resolve unintentional row multiplication?
- Q: How would you structure a data transformation pipeline using PySpark or pandas when processing raw data into structured tables?
- Q: What is your process for monitoring scheduled data pipelines, triaging job failures, and ensuring that partial executions are flagged correctly?
- Q: How do you design automated data quality checks to verify row counts, data freshness, and reconciliation back to source ERP or HR systems?
- Q: How have you used Git and version control practices when migrating local scripts into managed, production-ready code repositories?
- Q: How would you explain the operational purpose and structural differences between Bronze, Silver, and Gold layers in a lakehouse architecture like Microsoft Fabric?
- Q: What steps do you take when investigating a sudden discrepancy in a downstream business metric to isolate whether the root cause is in the source data or pipeline logic?
- Q: How do you document data pipelines, runbooks, and data catalogs so that engineering team members can maintain them easily?
- Q: What experience do you have working with ERP systems like SAP, Oracle, or IQMS/DELMIAworks, or integrating retail feeds for downstream analytics?
Application Tips
- Applicants may want to highlight their technical projects or professional background involving SQL, Python, PySpark, and Git directly on their resume. Highlighting experience with medallion lakehouse architectures (Bronze, Silver, Gold), cloud tools like Azure or Microsoft Fabric, or data quality framework implementation can showcase technical readiness. Early-career candidates, including recent graduates, can emphasize relevant data engineering internships, academic capstone projects, or personal code repositories to demonstrate practical knowledge. Candidates should also articulate their approach to investigating pipeline bugs and maintaining accurate business metrics.
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