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Aptean India
Lead EngineerAptean India • Delhi, India
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Lead Engineer

Lead Engineer

Aptean India • Delhi, India
20 days ago
Job description
Aptean 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

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