Data Operations Engineer

The Role: DataOps Engineer

As a DataOps Engineer, your responsibilities will span the development and implementation of automated solutions for data integration, quality control, and continuous delivery. This role demands a solid grounding in software engineering principles, fluency in programming languages such as Python or Scala, and an adeptness with DevOps tools. You'll play a crucial role in constructing and maintaining sophisticated data pipelines that support the organization's data science and analytics ambitions.

Collaboration is a cornerstone of this position. You will work closely with teams across the organization, assimilating their data requirements and challenges, and crafting agile, robust data solutions. Your efforts in implementing best practices in DataOps will aim to eliminate bottlenecks, elevate data quality, and ensure that data management processes are in tight alignment with our strategic analytics and decision-making objectives.

In this role, automating data pipelines and implementing scalable solutions will be just the beginning. You will also ensure data availability and integrity through effective governance, advocate for DataOps methodologies alongside IT and data teams, and continuously monitor, troubleshoot, and optimize data systems for superior performance.

 Skillset:-

Advanced proficiency in database technologies such as SQL Server, Oracle, MySQL, or PostgreSQL for data management and querying.

Expertise in implementing and managing data pipelines.

Strong understanding of data warehousing concepts, data modelling techniques, and schema design for building and maintaining data warehouses or data lakes.

Proficiency in cloud platforms such as AWS, Azure, or Google Cloud for deploying and managing scalable data infrastructure and services.

Knowledge of DevOps principles and practices for automating infrastructure provisioning, configuration management, and continuous integration/continuous deployment (CI/CD) pipelines.

Strong scripting and programming skills in languages like Python, Bash, or PowerShell for automation, data manipulation, and orchestration tasks.

Ability to collaborate with cross-functional teams including data engineers, data scientists, and business stakeholders to understand requirements, design data solutions, and deliver projects.

Excellent communication skills to effectively convey technical concepts to non-technical stakeholders and collaborate with team members.

Strong problem-solving skills to troubleshoot data issues, optimize performance, and improve reliability of data pipelines and infrastructure.

Ability to stay updated with emerging technologies, trends, and best practices in the field of DataOps and data engineering.

Initiative and drive to continuously improve skills, automate repetitive tasks, and streamline data operations processes for increased efficiency and productivity