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Data Engineer
Job Location:
Monthly Salary:
Posted:
12 October 2026 (15 hours ago)
Posted:
Application Deadline:
9 January 2027
Application Deadline:
Vacancies:
1 Vacancy
Vacancies:
Job Summary
This is a remote position.
Data Engineer
Summary
This pivotal role is dedicated to architecting constructing and maintaining the robust data infrastructure necessary to drive organizational insights. The successful candidate will serve as a cornerstone for data accessibility and integrity ensuring that complex datasets are efficiently processed and transformed into actionable intelligence. By bridging the gap between raw data sources and analytical applications this position plays a critical part in supporting strategic decision-making and optimizing operational workflows across the enterprise.
We are seeking a Data Engineer to join our Data & AI practice building modern data platforms for our clients on Microsoft Azure and Azure Databricks.
This role is well suited to an ambitious client-focused technologist who wants hands-on experience with the latest Microsoft data platform technologies - Microsoft Fabric and Azure Databricks - to deliver scalable pipelines that power analytics and AI.
You will work alongside architects data scientists and client stakeholders to ingest clean prepare and deliver trusted data and to build the APIs that connect that data to downstream applications and reporting.
Responsibilities
- Design develop and deploy scalable data pipelines to facilitate the seamless movement of information from diverse sources to centralized repositories.
- Collaborate closely with data scientists and analysts to understand specific data needs and deliver high-quality reliable data solutions.
- Monitor and optimize the performance of existing data systems to ensure maximum efficiency reliability and security.
- Implement data governance policies and standards to maintain data accuracy consistency and compliance with regulatory requirements.
- Troubleshoot and resolve complex data issues including data quality problems pipeline failures and integration challenges.
- Document technical processes data architectures and system specifications to ensure knowledge transfer and operational continuity.
Requirements
Data Pipeline Delivery
Design build and maintain batch and streaming data pipelines using Microsoft Fabric Azure Data Factory and
Azure Databricks with Python PySpark and SQL.
Ingest data from structured semi-structured and unstructured sources - databases APIs files and event streams -
into governed lakehouse environments.
Support the full data journey on client engagements: ingestion cleaning preparation transformation and delivery of
analytics-ready datasets.
Implement transformation logic aligned to Medallion (Bronze Silver Gold) architecture patterns in Azure Data Lake
Storage (ADLS Gen2) OneLake and/or Delta Lake.
Build and consume APIs and reusable data services that expose data to reporting applications and downstream
analytics.
Write clean well-tested code and participate in peer code reviews.
Data Modeling and Quality
Apply data modeling fundamentals (dimensional normalized star-schema) to structure data for analytics Power BI
and reporting use cases.
Implement data quality checks validation rules and monitoring to ensure pipelines are accurate complete and
reliable.
Troubleshoot data pipeline failures performance bottlenecks and data discrepancies escalating and resolving
issues promptly.
Document data flows data models and transformation logic for technical and client audiences.
Collaboration and Client Delivery
Work directly with client stakeholders analysts and data scientists to understand data requirements and translate
them into technical solutions.
Collaborate with senior engineers and architects to ensure your work aligns with broader platform and governance
standards.
Support data security access control and governance practices (e.g. Microsoft Purview role-based access).
Get exposure to how data is activated for front-line systems and early Data Agent/Copilot-style use cases.
Participate in agile ceremonies - sprint planning stand-ups and estimation - as part of a delivery team.
Qualifications
1-3 years of professional experience in data engineering or a related hands-on technical role (relevant internships
and academic projects considered for entry-level candidates).
Proficiency in Python and SQL with hands-on experience writing production-quality code.
Experience with PySpark and an understanding of distributed data processing.
Exposure to the Microsoft Azure data ecosystem (Azure Data Factory Microsoft Fabric ADLS Gen2) and/or Azure
Databricks; Delta Lake and Unity Catalog a plus.
Working knowledge of data modeling concepts and data warehousing fundamentals.
Understanding of ETL/ELT concepts and the end-to-end data lifecycle from ingestion through activation.
Familiarity with building or consuming APIs for data integration.
Familiarity with version control (Git) and basic CI/CD concepts (Azure DevOps a plus).
Strong analytical and problem-solving skills with close attention to data accuracy.
Comfortable communicating with both technical peers and over time client stakeholders.
Relevant certifications (e.g. Microsoft Azure Data Engineer Associate) are a plus but not required.
