Senior Data Engineer

JMAN Group; Chennai District; On-site; INR 800,000 - 1,500,000; Full time

Job summary

JMAN Group is searching for an experienced data engineer to design and implement data pipelines and manage data storage solutions. This role requires strong skills in cloud platforms and ETL tools, alongside excellent communication and problem-solving abilities.

The ideal candidate will have 7+ years of experience and be familiar with SQL, Python, and various data warehousing solutions. Join us in Chennai and contribute to our mission of delivering value through data and AI.

Qualifications

• 7+ years of experience in data platform build or related field.
• Familiarity with cloud-based data warehousing solutions.
• Proficient in SQL and Apache Spark / Python programming.

Responsibilities

• Design and implement data pipelines using ETL/ELT tools.
• Manage data storage solutions including databases and data warehouses.
• Develop and implement data quality checks.

Skills

Data platform build

Cloud-based data warehousing

SQL

Apache Spark

Python

AWS

Azure

GCP

ETL/ELT tools

Data visualization

Containerization technologies

DevOps methodologies

Communication

Collaboration

Problem-solving

Tools

Snowflake

Redshift

Databricks

Azure Data Factory

Power BI

Tableau

Docker

Kubernetes

Jira

Job description

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.

Technical Specifications

• 7 + years of experience in data platform build or any related field.
• Familiarity and has worked with cloud-based data warehousing solutions (e.g., Snowflake, Redshift, Databricks, Fabric) and principles
• Proficient in SQL and Apache Spark / Python programming languages.
• Experience with cloud platforms like AWS, Azure, or GCP.
• 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.
• Good to have skills include: 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.
• Excellent communication, collaboration, and problem-solving skills.

Qualifications

• 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.
• Ticketing & Workflow Tools: Freshdesk, Jira, ServiceNow, or similar.

Responsibilities

• 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., Git, CI/CD pipeline). 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.
• Work with business stakeholders to translate their needs into technical solutions. 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.