unifyCX - Senior Data Engineer - SQL/Python

Required Experience :

Preferred Skillsets :

- Very efficient in SQL/Stored procedures.

- At least 8 years of Hands on Experience in AWS Step function, AWS Lambda, AWS S3, AWS ECS, AWS Cloud Watch, AWS Event Bridge, AWS Athena, AWS Glue.

- At least 8 years of Experience in Terraform.

- At least 8 years of Experience in Jenkins pipelines(Groovy script).

- At least 8 years of experience in various kind of Data Scraping, Data Ingestion, Data

Processing.

- Good Experience in GIT, Bitbucket.

- Good Experience in Jira, Confluence and Scrum.

- Worked on end-to-end ETL projects.

- Have good understanding of data warehouse/data lake concepts.

- Very good at coding with best practices.

- Proactive, multitasker, Team Player, and can confidently collaborate with various teams like

data engineering, architecture, and data teams.

- Can lead and independently work on certain modules.

- Confidently lead and represent data team in client meetings and guide the juniors.

- Solution designing.

- Understand end to end data management tasks (not just ETL, for eg : scheduling,

performance, code optimisation, principles of data modelling, etc).

- Strong Problem Analysis & Solving skills.

- Effective Communication.

Key Responsibilities :

- Develop and optimize infrastructure as code using Terraform.

- Build and maintain CI/CD pipelines in Jenkins using Groovy scripting.

- Lead end-to-end ETL projects, ensuring best coding practices and high-quality delivery.

- Implement efficient data scraping, ingestion, and processing mechanisms for large-scale data.

- Collaborate effectively with data engineering, architecture, and analytics teams to design and

implement data solutions.

- Lead modules independently and provide technical guidance to junior engineers.

- Represent the data engineering team in client meetings, providing technical insights and

progress updates.

- Apply deep understanding of data warehousing and data lake concepts to design optimal solutions.

- Ensure scheduling, performance tuning, code optimization, and data modeling principles are

applied throughout data workflows.

- Utilize version control tools such as GIT and Bitbucket for source code management.

- Use Jira, Confluence, and Scrum methodologies to manage project workflows.

- Proactively identify and solve complex problems related to data management and

engineering.

- Communicate effectively across teams to align technical solutions with business objectives.