Mounika Nadendla

About

Data engineer focused on building reliable, production-grade data systems end-to-end: batch and real-time pipelines (Airflow/Kafka/Spark) with strong data quality, monitoring/alerting, and incident response. Has experience integrating external API/web data with retries, throttling, and schema-change handling, and serving curated datasets to analytics (Power BI) and backend consumers with performance optimizations like Redis caching.

Key Strengths

• Owned end-to-end data pipelines (ingestion → validation → transformations → serving) processing millions of records daily
• Implemented data quality controls (schema/null/integrity checks) that reduced inconsistencies and improved trust in reporting
• Built reliable orchestration/monitoring with Airflow (retries, SLAs, alerts) plus audit tables for anomaly detection
• Designed curated analytics datasets powering Power BI dashboards for business stakeholders
• External data collection integration with robust retries (exponential backoff), throttling/rate-limit handling, and schema-change resilience
• Improved API performance using Redis caching (~40% response-time reduction) and parallel/asynchronous processing
• Production incident handling: diagnosed Kafka consumer lag via Grafana/log correlation IDs; mitigated via rollback and added schema validation + DLQ

Reference Highlights

• Owned end-to-end data pipelines
• Delivered high reliability and data quality
• Strong monitoring and observability practices
• Effective at diagnosing and resolving pipeline failures (e.g., schema changes)
• Implemented fault-tolerant designs that reduced downtime
• Strong Python and SQL in production
• Performance- and cost-conscious optimization (BigQuery partitioning/clustering)
• Handled edge cases (null/inconsistent data) with standardized logic
• Ensured correctness via validation, reconciliation, and data quality checks
• Effective in ambiguous environments
• Breaks unclear requirements into structured designs
• Ships end-to-end solutions with minimal supervision
• Good tradeoff judgment (performance vs cost vs complexity)
• Minimized downstream impact through validation and controlled reprocessing
• Strong reliability and data-quality outcomes
• Built scalable ingestion processes
• Proactively handled schema changes
• Strong Python and SQL skills
• Solid complex querying and data modeling
• Builds reliable production transformations
• Mindful of performance considerations
• Handles edge cases well; writes accurate code
• Effective monitoring of pipelines
• Effective at handling failures
• Effective at managing backfills
• Minimizes downstream impact when issues occur
• Effective generalist in ambiguous environments
• Navigates unclear requirements
• Delivers end-to-end solutions
• Follows through reliably without heavy direction
• Efficient
• Good Python and SQL skills
• Good observability

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