Sai Kumar Manam

Data Engineer

Hyderabad $90/hr Remote 3+ yrs exp 91 · Outstanding

About

Data professional with 3+ years at Uber spanning restaurant-partner analytics and large-scale rides data engineering. Currently build streaming and batch pipelines (Spark, Kafka, Hive) processing several terabytes of trip data daily and cut pipeline processing time 40% through query and partitioning optimization. Previously drove partner-level analytics for 150+ Uber Eats restaurant accounts. Specializes in AWS-native ETL (Glue, S3, Redshift) and Medallion-architecture data modeling for analytics-ready datasets.

Skills & Expertise (23)

Apache Spark Advanced

8.3/10

Years Exp

Data Pipeline Design Advanced

8.0/10

Years Exp

Python Advanced

8.0/10

Years Exp

SQL Advanced

8.0/10

Years Exp

Apache Airflow Intermediate

7.5/10

Years Exp

Kafka Intermediate

7.5/10

Years Exp

Postgresql Intermediate

7.0/10

Years Exp

Tableau Intermediate

6.5/10

Years Exp

Google Sheets Lambda Google Data Studio Git Liquibase Harness Hive Hadoop Spark SQL PySpark SNS CloudWatch Redshift Glue S3

Work Experience

Data Engineer

Uber

Jun 2025 - Present

Design and implement batch and streaming pipelines (Apache Spark, Hive, HDFS, Kafka) processing several terabytes of trip and telemetry data daily to power rider- and driver-facing analytics. Architect a Medallion-based (Bronze/Silver/Gold) data platform ingesting real-time Kafka trip-event streams into analytics-ready Gold datasets used by 3 downstream analytics teams. Build end-to-end AWS ETL pipelines (Glue, S3, Redshift) supporting trip-level reporting and business intelligence workloads. Optimized existing Spark and Hive pipelines, reducing processing time by 40% through query optimization, partitioning strategy, and resource tuning. Developed a trend-based data quality framework that validated monthly ride metrics against historical patterns and flagged anomalies, cutting undetected data issues by close to 20%. Designed Silver-layer fact/dimension tables (driver, trip, rider) with PII governance, and Gold-layer datasets enabling analysis of trip completion rate and handling time. Deploy data pipeline code through existing CI/CD pipelines in Harness, and manage SQL schema changes using Liquibase for version-controlled, repeatable database migrations. Participate in code review and release approval steps within the Harness CI/CD workflow to ensure safe, incremental rollouts of pipeline changes to production. Collaborate with data science and product stakeholders to define schema contracts and SLAs for new Goldlayer datasets, supporting 2-3 new analytics use cases per quarter. Write unit and integration tests for critical pipeline components, reducing production incidents caused by upstream schema changes. Review pull requests and pair with two junior team members on Spark job design and Hive query optimization.

Data Analyst

Uber

Aug 2023 - May 2025

Analyzed restaurant partner performance data (order volume, delivery SLAs, cancellation trends) using SQL and Hive across 150+ restaurant partners to surface growth and retention insights for the Eats partner team. Automated a recurring reporting workflow extracting data from Hive tables into Google Sheets dashboards and auto-generated Google Slides decks via Apps Script, cutting manual reporting effort by about 5 hours per week. Ingested and validated raw restaurant partner CSV feeds from GCP Cloud Storage into HDFS, building external Hive tables to support ad hoc and recurring analysis. Partnered with cross-functional stakeholders to define and track partner-level KPIs (order completion rate, handling time, submission accuracy) across first- and third-party restaurant data sources. Built Google Data Studio and Google Sheets dashboards used in weekly business reviews to monitor restaurant partner operational health, and reconciled incoming data feeds, cutting discrepancy tickets by close to 20%. Conducted ad hoc deep-dive analyses on restaurant partner churn and reactivation trends, presenting findings to the Eats partner team during monthly reviews. Documented SQL and Hive querying standards for the team and onboarded 2 new analysts onto existing reporting workflows. Wrote and maintained complex SQL queries and reusable Hive views to standardize recurring restaurant partner reports, cutting down repetitive ad hoc query requests from stakeholders. Created and optimized SQL views and summary tables joining order, delivery, and partner data for faster self-serve reporting by the business team.