- Lead the design and implementation of modern data platforms across Azure, AWS, and Snowflake.

- Translate business requirements into robust technical solutions covering ingestion, transformation, integration, warehousing, and validation.

- Architect, build, and maintain data pipelines for analytics, reporting, and machine learning use cases.

- Develop and maintain ETL processes to move data from multiple sources into cloud data lakes and warehouses.

- Design and implement data models, lineage, and metadata management to ensure consistency and traceability.

- Optimize pipelines and workflows for performance, scalability, and cost efficiency.

- Enforce data quality, security, and governance standards across all environments.

- Support migration of legacy/on-premises ETL solutions to cloud-native platforms.

- Develop and tune SQL queries, database objects, and distributed processing workflows.

- Drive adoption of CI/CD, test automation, and DevOps practices in data engineering.

- Collaborate with architects, analysts, and data scientists to deliver end-to-end data solutions.

- Provide technical leadership, mentorship, and training to junior engineers.

- Produce and maintain comprehensive technical documentation.

Requirements & Skills :

- Strong experience designing and developing ETL/data pipelines on Azure, AWS, and Snowflake.

- Proficiency in SQL, Python, and distributed processing (e.g., Spark, Databricks, EMR).

- Hands-on expertise with :

- Azure : Data Factory, Synapse, Databricks, Azure SQL

- AWS : Glue, Redshift, S3, Lambda, EMR

- Snowflake : Data warehousing, performance optimization, security features

- Solid understanding of data modeling, lineage, metadata management, and governance.

- Experience with CI/CD, infrastructure-as-code, and automation frameworks.

- Strong problem-solving and communication skills with the ability to work across teams.

Desired Profile :

- Bachelors or masters degree in computer science, Engineering, or related discipline.

- 6 - 10 years of progressive data engineering experience, with at least 5 years in cloud-based data platforms.

- Strong expertise in data modelling, database design, and warehousing concepts.

- Proficiency in Python (including Pandas, API integrations, and automation).

- Familiarity with varied data formats and sources (CSV, Parquet, JSON, APIs, relational and NoSQL databases).

- Exposure to modern orchestration and workflow tools, with strong understanding of CI/CD practices.

- Experience with Databricks and Microsoft Fabric is a plus.

- Excellent analytical, problem-solving, and communication skills.

- Ability to evaluate new technologies and adopt them where appropriate.