Job descriptionAptean is seeking a hands-on, results-driven
Data Engineering professional
to design, build, and maintain scalable Enterprise data lakehouse using
Microsoft Fabric , following the
medallion architecture . This role is key to advancing our enterprise data platform, enabling analytics, Enterprise reporting, and AI-driven insights. The ideal candidate will have strong experience in
data modeling, data lakehouse technologies , and a passion for leveraging
AI
to optimize and enrich data processes wherever possible.
PRINCIPAL DUTIES AND RESPONSIBILITIES
Design and build robust, scalable data ingestion pipelines using Microsoft Fabric (Pipelines, Dataflows, Notebooks) to integrate data from Business Applications and external APIs. Perform deep source system analysis to define ingestion strategies that ensure data reliability, consistency, and observability, while applying metadata-driven design for automation. Develop and maintain Delta Tables using the
medallion architecture
(bronze/silver/gold) to systematically cleanse, enrich, and standardize data for downstream consumption. Implement comprehensive data quality checks (nulls, duplicates, schema drift, outliers, SCD types) and ensure data integrity across all transformation layers in the Lakehouse. Apply governance practices including schema versioning, data lineage tracking, role-based access control (RBAC), and audit trails to ensure compliance, traceability, and secure data access. Build semantic models and define business-aligned KPIs to support self-service analytics and dashboarding in Power BI and other BI platforms. Structure the gold layer and semantic model to support
AI/ML use cases , ensuring datasets are enriched, contextualized, and optimized for AI agent consumption. Develop and maintain
AI-ready run flows and access patterns
to enable seamless integration between the Lakehouse and AI agents for tasks such as prediction, summarization, and decision automation. Implement DevOps best practices for pipeline versioning, testing, deployment, and monitoring; proactively detect and resolve data integration and processing issues. Collaborate cross-functionally with analysts, data scientists, and business users to ensure the data platform supports evolving needs for analytics, operational reporting, and AI innovation.
Work Experience 7-10 Year’s
Knowledge, Skills, Abilities & Competencies Deep expertise in data engineering
with hands-on experience in designing and implementing large-scale data platforms, including
data warehouses, lakehouse , and modern
ETL/ELT pipelines . Proven ability to build, deploy, and troubleshoot
highly reliable, distributed data pipelines
integrating structured and unstructured data from various internal systems and external sources. Strong technical foundation in
data modeling, database architecture, and data transformation techniques
using medallion architecture (bronze/silver/gold layers) within Microsoft Fabric or similar platforms. Solid understanding of
data lakehouse patterns
and
Delta Lake / OneLake
concepts, with the ability to structure data models that are
AI/ML-ready
and support semantic modeling. Experience implementing
data quality frameworks
including checks for nulls, duplicates, schema drift, outliers, and slowly changing dimensions (SCD types). Familiarity with
data governance , including schema versioning, data lineage, access controls (RBAC), and audit logging to ensure secure and compliant data practices. Working knowledge of
data visualization tools
such as
Power BI
with the ability to support and optimize semantic layers and KPI definitions. Strong communication and collaboration skills, with the ability to articulate complex data engineering solutions to both technical and non-technical stakeholders, and to lead cross-functional initiatives. Experience with
DevOps practices , including version control, CI/CD pipelines, environment management, and performance monitoring in a data engineering context. Shift details : Required to work in shift:
YES If Yes Shift Timing:
1pm to 10pm