About the Company JMAN Group is a fast-growing data engineering & data science consultancy. We work primarily with Private Equity Funds and their Portfolio Companies to create commercial value using Data & Artificial Intelligence. In addition, we also work with growth businesses, large corporates, multinationals, and charities.

We are headquartered in London with Offices in New York, London and Chennai. Our team of over 450 people is a unique blend of individuals with skills across commercial consulting, data science and software engineering.

In May 2023 we took a minority investment from Baird Capital and in January 2024 we opened an office in New York with the ambition of growing our US business to be as large as, if not bigger than, our European business by 2027.

7 + years of experience in data platform build or any related field.

Familiarity and has worked with cloud-based data warehousing solutions (e.g., Fabric,Snowflake, Redshift, Databricks) and principles
Proficient in SQL and Apache Spark / Python programming languages.
Experience with cloud platforms like Azure.
Experience in data pipelines and ETL/ELT tools, including AWS Glue/Azure Data Factory/ Synapse/Matillion/DBT
Experience in implementing or working with data governance frameworks and practices to ensure data integrity and regulatory compliance.
Knowledge of data quality tools and practices.
Data visualization using Power BI, Tableau, or Looker, and familiarity with full-stack technologies.
Experience with containerization technologies (e.g., Docker, Kubernetes)
Experience with CI/CD pipelines and DevOps methodologies.
ETL or ELT: Azure Data Factory, Databricks, Synapse, dbt (any two – Mandatory).

Data Warehousing: Azure SQL Server/Redshift/Big Query/Databricks/Snowflake (Anyone - Mandatory).
Data Visualization: Looker, Power BI, Tableau (Basic understanding to support stakeholder queries).
Cloud: Azure (Mandatory), AWS or GCP (Good to have).
SQL and Scripting: Ability to read/debug SQL and Python scripts.
Monitoring: Azure Monitor, Log Analytics, Datadog, or equivalent tools.
Freshdesk, Jira, ServiceNow, or similar.
DevOps: Docker, Kubernetes), Git, CI/CD pipelines (Exposure preferred).

Design and implement data pipelines using ETL/ELT tools and techniques.
Configure and manage data storage solutions, including relational databases, data warehouses, and data lakes.
Develop and implement data quality checks and monitoring processes.
Automate data platform deployments and operations using scripting and DevOps tools (e.g., Ensuring compliance with data governance and security standards throughout the data platform development process.
Troubleshoot and resolve data platform issues promptly and effectively.
Collaborate with the Data Architect to understand data platform requirements and design specifications.
Assist with data modelling and optimization tasks.
Document the data platform architecture, processes, and best practices.
Stay up to date with the latest trends and technologies in full stack development, data engineering, and DevOps.
Proactively suggest improvements and innovations for the data platform.

Senior Data Engineer - AWS, DevOps

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