Responsibilities
- To provide solutioning and drive the implementation of DOPS features which includes - building IAC infra, Git Lab re-structuring, Git lab Upgrades monitoring etc
- Have the vision on data strategy and able to deliver the same.
- Able to lead the design and implementation of data management processes, including data sourcing, integration, and transformation.
- Able to manage and lead a team of data professionals, providing guidance, mentoring and foster a collaborative and innovative team culture focused on continuous improvement.
- To evaluate and recommend data-related technologies, tools, and platforms.
- Collaborate with IT teams to ensure seamless integration of data solutions.
- Implement and enforce data security protocols and ensure compliance with relevant regulations.
- Ability to work in team in diverse/ multiple stakeholder environment.
- Ability to communicate complex technology solutions to diverse teams namely, technical, business and management teams.
Required Qualifications:
- Bachelor’s degree in computer science or STEM (science, technology, engineering, or mathematics)/ related field.
- At least 8+ years of strong data warehousing experience using RDBMS and Non-RDBMS databases.
- At least 5 years of recent hands-on professional experience (actively coding) working as a data engineer (back-end software engineer considered).
- Strong AWS knowledge in terms of designing new architecture and providing optimized solution for existing one.
- Professional experience working in an agile, dynamic and customer facing environment is required.
- Understanding of distributed systems and cloud technologies (AWS) is highly preferred.
- Understanding of data streaming and scalable data processing is preferred to have.
- Experience with large scale datasets, data lake and data warehouse technologies such as AWS Redshift, Google BigQuery, Snowflake. In-depth knowledge of Snowflake and its architecture is preferred.
- Atleast 2+ years of experience in ETL (AWS Glue), Amazon S3, Amazon RDS, Amazon Kinesis, Amazon Lambda, Apache Airflows, Amazon Step Functions.
- Strong knowledge in scripting languages like Python, UNIX shell and Spark is required.
- Understanding of RDBMS, Data ingestions, Data flows, Data Integrations etc.
- Technical expertise with data models, data mining and segmentation techniques.
- Experience with full SDLC lifecycle and Lean or Agile development methodologies.
- Knowledge of CI/CD and GIT Deployments.
