Senior Data Engineer

As a Senior Data Engineer, you will play a crucial role in designing, implementing, and maintaining our data infrastructure. you'll work with modern data engineering technologies and cloud platforms to build scalable ETL pipelines, optimize data processing workflows, and ensure data quality and reliability across our systems. This role extends significantly into the ML domain, where you'll be responsible for building and maintaining feature engineering pipelines that power various ML models across the organization. you'll work on real-time feature generation systems, deploy and maintain ML model infrastructure, and ensure seamless integration between data pipelines and ML workflows. Your work will directly impact model performance and enable data scientists to focus on model development rather than infrastructure concerns.

• Partner closely with business stakeholders to understand and translate data warehouse requirements into technical solutions

• Provide comprehensive support for data warehouse initiatives including requirements gathering, solution design, and implementation

• Optimize data warehouse performance through query optimization, indexing strategies, and architectural improvements

• Design, develop, and maintain robust ETL/ELT pipelines using modern data engineering tools

• Implement real-time and batch data processing solutions to meet business requirements

• Develop and maintain data models and DataMart solutions for analytics and reporting needs

• Deploy and maintain ML model infrastructure ensuring scalability and reliability

• 3-6 years of experience in Data Engineering

• Strong expertise in distributed computing and advanced problem-solving/analytical skills

• Deep understanding of data warehouse design, optimization, and performance tuning

• Expertise in Python, SQL and NoSQL databases

• In-depth experience in Could services, preferably AWS (EMR, S3, Redshift, MSK)

• Expertise in Data engineering tech stack like Spark (PySpark), Airflow, Debezium, Hadoop

• Experience in data modelling, DataMart development

• Understanding of ML model deployment and maintenance workflows with feature store architectures and best practices

• Good to Have - Experience in setting up and working with On-premises Data Engineering tech stack

• A positive, get-things-done workplace

• A dynamic, constantly evolving space (change is par for the course important you are comfortable with this).

• An inclusive environment that ensures we listen to a diverse range of voices when making decisions.

• Ability to learn cutting edge concepts and innovation in an agile start-up environment with a global scale.

• Access to 5000+ training courses accessible anytime/anywhere to support your growth and development (Corporate with top learning partners like Harvard, Coursera, Udacity).