
The Business Operations team is seeking an intern to help run the daily engine that keeps our sales pods accountable and drives how we scale. This role offers exposure to the full BizOps stack — compliance auditing, process improvement, sales enablement, and the BPM and automation work that is transforming manual operations into streamlined workflows. Ideal candidates are sharp, operations-minded, and eager to build a foundation at the intersection of business operations and process automation.
Responsibilities:
- Run daily and weekly compliance audits across our sales-pod SOPs, upholding consistency and quality across every pod
- Review CRM records - call logs, notes, next steps, task completion, and data completeness - to keep pipeline data accurate and decision-ready
- Turn audit findings into signal, surfacing the recurring gaps and bottlenecks that point to where process should be tightened, standardized, or automated
- Feed the BPM and automation initiatives, helping identify manual checks that can move into automated workflows and validating that they work as intended
- Support sales enablement by helping keep the SOPs, trackers, and playbooks our pods rely on sharp and current
- Assist with documentation of business processes, workflows, and team procedures

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Job Description – Lead Data Engineer (AWS + Big Data)
Location: Pune - Hybrid
Experience: 8+ Years
Role Overview
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Key Responsibilities
- Design, develop, and maintain scalable data pipelines and ETL/ELT solutions on AWS.
- Build and optimize Big Data applications using Spark/PySpark, HBase, Hive, Kafka, and related technologies.
- Work with AWS services such as S3, Glue, Lambda, IAM, and CloudWatch for cloud-native data engineering.
- Lead technical implementation, mentor engineers, conduct code reviews, and drive engineering best practices.
- Ensure data quality, performance optimization, CI/CD adoption, and production support.
Required Skills
- Strong hands-on experience with AWS (S3, Glue, Lambda, IAM, CloudWatch)
- Apache Spark (PySpark/Scala) and Big Data ecosystem
- Databricks exposure (mandatory)
- HBase (strongly preferred)
- Python or Java, Advanced SQL
- ETL/ELT development and Data Warehousing concepts
- Apache Airflow or similar orchestration tools
- Git, CI/CD, Performance Tuning, and Data Quality
Good to Have
- Kafka / Spark Structured Streaming
- Hive, Impala, Hadoop ecosystem
- Delta Lake / Lakehouse concepts
- Snowflake or other modern cloud data platforms
Experience Required
- 8–14 years of Data Engineering experience with strong AWS implementation expertise.
- Proven experience leading technical delivery and mentoring engineering teams.
- Strong understanding of enterprise-scale data platforms and Big Data architectures.
- Ability to collaborate with architects, stakeholders, and cross-functional teams to deliver scalable solutions.
NOTE: Final Technical round is mandatory to be taken F2F from Pune, office.
JD for Sales and Marketing:
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We would love to connect with your.
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Weekends: Off
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Lucarative Incentives
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Shift timings: US & UK Shift
Working days: Monday to Saturday
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