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Job Description

Key Responsibilities :


Data Pipeline Development :


- Design, build, and maintain highly efficient and scalable ETL/ELT pipelines to process large volumes of structured and unstructured data


- Implement real-time and batch data processing solutions using modern data engineering tools (e.g., Fabric, Spark, Kafka, Airflow, )

- Optimize data workflows for performance, reliability, and cost-effectiveness

- Monitor pipeline health and implement automated alerting and recovery mechanisms

Data Architecture & Infrastructure :


- Collaborate with architects to design and implement data warehouse and Data Warehouse, Data Lake, and Lakehouse solutions (e.g., Fabric, Snowflake).

- Build and maintain cloud-based data infrastructure (Azure or AWS, GCP)

- Implement data governance frameworks and ensure compliance with data privacy regulations


- Design schema and data models for optimal query performance and scalability

Data Quality and Integration :


- Develop data validation frameworks and implement quality checks throughout pipelines

- Integrate data from multiple sources including APIs, databases, streaming platforms, and third-party services

- Create and maintain data documentation, lineage tracking, and metadata management

- Troubleshoot data quality issues and implement corrective measures

Collaboration & Support :


- Partner with data scientists, analysts, and business stakeholders to understand data requirements

- Support analytics teams with data access, modeling, and optimization

- Participate in code reviews and maintain high standards for data engineering practices

- Mentor junior team members and contribute to team knowledge sharing

Skills :


- Knowledge of machine learning workflows and MLOps practices

- Familiarity with data visualization tools (Tableau, Looker, Power BI)

- Experience with stream processing and real-time analytics

- Experience with data governance and compliance frameworks (GDPR, CCPA)

- Contributions to open-source data engineering projects

- Relevant Cloud certifications (e.g., Microsoft Certified : Azure Data Engineer Associate, AWS

Certified Data Engineer, Google Cloud Professional Data Engineer).

- Specific experience or certifications in Microsoft Fabric, or Dataiku, Snowflake.

Required Qualifications :


Technical Skills :


- Programming Languages : Proficiency in Python


- Cloud Platforms : Hands-on experience with Azure (Fabric, Synapse, Data Factory, Event Hubs)

- 3+ years of experience in data engineering or related roles with 5+ years of Software Engineering Experience.

- Databases : Strong SQL skills and experience with both relational (Microsoft SQL Server, PostgreSQL, MySQL) and NoSQL (MongoDB, Cassandra) databases

- Version Control : Proficiency with Git and collaborative development workflows

- Proven track record of building production-grade data pipelines handling large-scale data or solutions.

- Desired experience with containerization (Docker) and orchestration (Kubernetes) technologies.


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