Monitor the health and performance of data systems, including databases, data warehouses, and data lakes.
Conduct root cause analysis and implement corrective actions to prevent recurrence of issues.
Manage and optimize data infrastructure components such as servers, storage systems, and cloud services.
Develop and implement data quality checks, validation rules, and data cleansing procedures.
Implement security controls and compliance measures to protect sensitive data and ensure regulatory compliance.
Design and implement data backup and recovery strategies to safeguard data against loss or corruption.
Optimize the performance of data systems and processes by tuning queries, optimizing storage, and improving ETL pipeline efficiency.
Maintain comprehensive documentation, runbooks, and fix guides for data systems and processes.
Collaborate with multi-functional teams, including data engineers, data scientists, business analysts, and IT operations.
Lead or participate in data-related projects, such as system migrations, upgrades, or expansions.
Deliver training and mentorship to junior team members, sharing knowledge and standard methodologies to support their professional development.
Participate in rotational shifts, including on-call rotations and coverage during weekends and holidays as required, to provide 24/7 support for data systems, responding to and resolving data-related incidents in a timely manner
Hands-on experience with source version control, continuous integration and experience with release/organizational change delivery tools.
About You
Basic Qualifications:
6+ years of experience designing and building scalable and robust data pipelines to enable data-driven decisions for the business.
BE/Masters in computer science or equivalent is required
Other Qualifications:
Prior experience with CRM systems (e.g. Salesforce) is desirable
Experience building analytical solutions to Sales and Marketing teams.
Experience with very large-scale data warehouse and data engineering projects.
Experience developing low latency data processing solutions like AWS Kinesis, Kafka, Spark Stream processing.
Should be proficient in writing advanced SQLs, Expertise in performance tuning of SQLs
Experience working with AWS data technologies like S3, EMR, Lambda, DynamoDB, Redshift etc.
Solid experience in one or more programming languages for processing of large data sets, such as Python, Scala.
Good to have experience working on Snowflake , Fivetran DBT and Airflow
Ability to create data models, STAR schemas for data consuming.
Extensive experience in troubleshooting data issues, analyzing end to end data pipelines and working with users in resolving issues
