Data Engineer

Aptean India; Bengaluru; On-site; INR 800,000 - 1,200,000; Full time

Job summary

Aptean India is seeking a hands-on Data Engineer to design, build, and maintain a scalable Enterprise data lakehouse using Microsoft Fabric. You will play a crucial role in advancing the enterprise data platform.

The candidate should have a Bachelor's degree and 2-5 years of experience, focusing on building robust data pipelines and implementing data governance practices. Knowledge of SQL and data modeling is essential.

Qualifications

• 2-5 years of experience in data engineering.
• Hands-on experience in data warehouse and lakehouse designs.
• Knowledge of ETL/ELT pipelines development.

Responsibilities

• Design and build robust data ingestion pipelines.
• Perform source system analysis for data integration.
• Implement data quality checks and ensure data integrity.
• Apply data governance practices.
• Provide support for AI/ML use cases.

Skills

Designing and implementing data platforms

Microsoft Fabric

Data pipeline troubleshooting

Medallion architecture

SQL and data modeling

Education

Bachelor's degree

Master's degree

Tools

Delta Lake

Git

Azure DevOps

Job description

Preferred Degree/Certification

Bachelor’s degree (required). Master’s degree (preferred). Ph.D., JD, Certification, Registration, Licensure, Other: not required.

Overview

Aptean is seeking a hands‑on, results‑driven Data Engineer to design, build, and maintain a scalable Enterprise data lakehouse using Microsoft Fabric, following the medallion architecture.

Job Summary

Key role to advance our enterprise data platform, enabling analytics, reporting, and AI‑driven insights.

Principal Duties and Responsibilities

• Design and build robust, scalable data ingestion pipelines using Microsoft Fabric (Pipelines, Dataflows, Notebooks) to integrate data from Business Applications, databases, files, and APIs into the Lakehouse.
• 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.
• 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.
• Leverage DevOps best practices for pipeline versioning, testing, deployment, and monitoring; proactively detect and resolve data integration and processing issues.

Education

Minimum: Bachelor’s degree; Preferred: Master’s degree.

Work Experience

2-5 Years.

Knowledge, Skills, Abilities & Competencies

• Hands‑on experience in designing and implementing data platforms, including data warehouses, lakehouse, and modern ETL/ELT pipelines.
• Working knowledge of Microsoft Fabric (Pipelines, Dataflows Gen2, Notebooks, Lakehouse) is strongly preferred.
• Proven ability to build, deploy, and troubleshoot highly reliable, distributed data pipelines integrating structured and unstructured data from various internal systems and external sources.
• Working knowledge of the medallion architecture (bronze/silver/gold) and Delta Lake / OneLake concepts; prior project experience implementing this pattern is highly desirable.
• 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.
• Solid understanding of SQL, relational and dimensional data modeling, query tuning, and basic performance optimization.
• Familiarity with data quality concepts — null/duplicate/schema‑drift checks, basic SCD handling, and validation rules in transformation pipelines.
• Familiarity with Git, branching strategies, and CI/CD concepts in a data engineering context (Azure DevOps or GitHub Actions).
• 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.

Shift Details

Required to work in shift: NO. If Yes, timing: N/A.

Reports To

Kshitij Bhatia

Disclaimer

The preceding job description has been designed to indicate the general nature and level of work performed by employees within this classification. It is not designed to contain or be interpreted as a comprehensive inventory of all duties, responsibilities and qualifications required of employees assigned to this job.