Meghana Gogineni

About

Senior Data Engineer with 4+ years of experience in analytics engineering, backend data infrastructure, and robust ELT pipeline development. Owns and optimizes dbt production model libraries with testing, documentation, lineage, and metric governance, with strong SQL and Snowflake expertise. Has built finance/operations data spines and established data quality standards and standards-based analytics engineering for executive, board, and investor reporting. Background includes scaling PySpark ETL, Kafka streaming ingestion, and orchestration automation using Airflow, Jenkins, and CI/CD in enterprise environments.

Experience

• Senior Data Engineer · Athenahealth02-01-2025

   Owned and optimized the dbt model library, ensuring models are clean, thoroughly tested, well-documented, and aligned with business requirements, including new data sources like Heap and GA4.  Managed and enhanced the ELT stack, encompassing Stitch, Snowflake, dbt, and associated orchestration tools, with a strong focus on reliability, performance, and clear data lineage.  Developed and maintained a robust finance and operations data spine on Snowflake, serving as the authoritative source of truth for core executive KPIs and metric definitions.  Defined and implemented analytics engineering standards, made key tooling decisions, and shaped the long-term roadmap for the data platform, prioritizing thoughtful architecture and data quality.  Collaborated cross-functionally with Engineering, FP&A, and Accounting teams to translate technical infrastructure into precise business needs and vice versa.  Established and enforced data quality standards, developed comprehensive testing frameworks, and implemented metric governance practices across the data ecosystem.  Provided critical data insights and reporting directly supporting executive, board, and investor decision-making, ensuring data integrity for high-stakes activities.  Led sprint planning and prioritization for the BI request queue in close collaboration with the BI team and various cross-functional stakeholders.  Automated over 150 Airflow workflows for pipeline orchestration and dependency management while integrating CI/CD pipelines with Jenkins and GitHub for streamlined deployments.

• Data Engineer · Morgan Stanley06-01-2022 – 12-31-2023

   Designed and implemented scalable data engineering solutions using dbt and Snowflake, supporting financial risk management and regulatory reporting platforms.  Developed PySpark-based ETL pipelines, processing over 3TB of financial transaction data daily, with a focus on reliability and performance within a Snowflake environment.  Built ingestion frameworks to load critical financial data from Oracle and SQL Server into Snowflake, ensuring seamless integration and data availability.  Designed and optimized Snowflake ELT pipelines, significantly improving warehouse performance and query execution efficiency by 35% for analytical use cases.  Implemented Kafka-based streaming solutions to ingest over 10 million financial events daily, ensuring near real-time data for critical business operations.  Developed advanced SQL transformations and aggregation frameworks within Snowflake, supporting complex enterprise reporting and analytical requirements.  Implemented rigorous data quality validation and reconciliation processes, reducing reporting discrepancies by 30% through comprehensive testing frameworks.  Automated workflow scheduling through Apache Airflow, integrating deployments using Jenkins and GitHub to enhance operational efficiency and data pipeline reliability.  Collaborated extensively with business stakeholders in Agile Scrum environments, using Jira throughout all phases of the SDLC to deliver impactful data solutions.

• Junior Data Engineer · Walmart02-01-2021 – 05-31-2022

   Assisted in the development of robust ETL pipelines using Python and SQL for retail sales and inventory analytics, focusing on data quality and accessibility.  Built ingestion workflows to load data from Oracle and PostgreSQL into centralized reporting environments, ensuring data integrity and consistency.  Developed Hive-based transformations processing over 500GB of retail data daily, contributing to the foundation of analytical reporting.  Created dimensional data models supporting comprehensive sales, inventory, and customer reporting solutions for various business units.  Performed extensive data cleansing, profiling, and validation activities, significantly improving overall data quality by 20% for downstream consumption.  Developed advanced SQL queries, views, and stored procedures to meet diverse business reporting requirements and enhance data accessibility.  Assisted with Google Cloud Platform (BigQuery) data migration initiatives and supported enterprise data lake implementations on BigQuery.  Created Power BI datasets and dashboards, translating complex data into actionable insights for business stakeholders.  Participated actively in Agile development processes using Jira for sprint planning and project tracking, ensuring timely delivery of data solutions.

Education

• The University of Texas at ArlingtonMaster of Science, Data Science

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