Data Engineering & Technical Lead

HEMANTH DEVISETTI

E-Mail: *******.*********@*****.*** Mobile: +1-312-***-**** Snowflake Certified Snowpro Core LinkedIn: www.linkedin.com/in/hemanth-devisetti-76b48ccc Data Engineer DBA Lead ETL Developer Business Analytics 19+ years of experience

Data Architecture Design

Performance Optimization

Data Quality Management

Data Integration & Database Migration

Project Management

Data Wrangling

Database Administration

Data Security & Compliance

Business Alignment

ETL/Database Design & Development

Data Transformation

Data Enrichment

RBAC Model

Troubleshooting Bottlenecks

Requirement Gathering

Analytical Thinking

Problem-Solving

Team Management

Seasoned IT professional with experience specializing in data engineering, ETL development, and business analysis across diverse industries, including Banking, Retail, Telecom, and Pharmaceuticals.

PROFILE SYNOPSIS

Extensive experience in Snowflake, Informatica PowerCenter, IICS, PySpark, Databricks and Teradata, ensuring seamless data integration, administration, transformation, and migration.

Designed and implemented enterprise Data Lake and Data Warehouse architectures using AWS S3/Azure Data Lake Storage, Databricks and Snowflake, enabling scalable ingestion, transformation, governance, and analytics for structured and semi-structured data.

Strong track record in architecting and optimizing enterprise-scale data solutions. Skilled in designing and implementing ETL workflows, data pipelines, and cloud-based data warehouse architectures to drive efficiency, scalability, and performance.

Proficient in SQL development, BTEQ scripting, and data modelling, with hands-on expertise in Teradata tools such as MultiLoad, FastLoad, FastExport, and TPump for high-volume data operations and seamless database migration.

Adept at designing and implementing large-scale Big Data solutions using Hadoop, Hive, and Databricks, focusing on data ingestion, cleansing, transformation, and analytics.

Proficient in performance tuning, optimizing SQL queries, virtual warehouses, and storage strategies, and leveraging indexing, partitioning, and clustering techniques for cost-efficient data processing.

A collaborative leader with a strong track record in cross-functional stakeholder engagement, system integration, and business analysis, aligning data-driven strategies with organizational goals to enhance decision-making and operational efficiency.

Managed end-to-end Snowflake platform administration, including user and security management (RBAC/MFA), warehouse administration and performance tuning, cost optimization, database and schema management, monitoring and incident resolution, automation (Tasks, Streams, Snowpipe), data sharing, backup and disaster recovery, governance, production support, and client/stakeholder coordination to ensure a secure, highly available, and optimized Snowflake environment.

Architected & designed robust, scalable data pipelines & data warehouse systems to handle large volumes of data from diverse sources. Led and mentored junior data engineers, assigning tasks, providing technical guidance, and reviewing code.

Engineered and maintained scalable ETL workflows using Informatica PowerCenter and IICS, ensuring seamless data integration and high-quality transformation

Owned data engineering projects, defining project scope, timelines & deliverables, and collaborating with stakeholders to ensure successful project execution.

Consolidated and migrated data from multiple sources, including relational databases, cloud storage, and flat files, to enhance accuracy, consistency, and processing efficiency.

Built data integration processes, including ETL/ELT workflows to transform and load data into data warehouses and data lakes.

Collaborated with business teams to understand data needs, translate business requirements into technical specifications, and deliver data solutions that drive business insights.

Enhanced Snowflake performance by optimizing queries, implementing clustering and partitioning techniques, and refining execution plans to reduce operational costs.

Designed and orchestrated ETL pipelines leveraging Snowflake SQL, streamlining data ingestion, transformation, and loading into the Snowflake data warehouse while upholding data integrity and governance standards.

Performed data profiling, assessment, and feasibility analysis to ensure a seamless and efficient migration process.

Orchestrated and integrated services such as AWS Lambda, Amazon ECS, and custom applications via APIs to streamline workflows and enhance system interoperability.

Conducted in-depth data analysis in Teradata to identify tables, relationships, and dependencies essential for a successful migration to Snowflake while maintaining data integrity.

TECHNICAL SKILLS

Databases: Snowflake, Teradata, Azure Databricks, Oracle, Netezza, DB2

Big Data Frameworks: Hadoop (HDFS, Map Reduce), Apache Hive, Spark

Tools: Informatica, IICS, Datastage, Ab Initio, SAP Power Designer Operating Platforms: Unix, Windows, Mainframes

Cloud Technologies: AWS, Azure, GCP Languages: VB.NET, C#.NET, XML, Shell Scripting, SQL, PL/SQL, Python, SnowSQL

Application Utilities: BTEQ, Multi-Load, Fast Load, Fast Export, TPUMP

ACADEMIC DETAILS

Master of Science in Software Engineering, BITS Pilani, 2010

Bachelor of Technology in Electrical & Electronics Engineering, Jawaharlal Nehru Technological University, Hyderabad, 2007

PROJECTS HANDLED

CITY NATIONAL BANK, Jersey City, NJ Duration: Apr 2025 – Till date Role: Snowflake Lead Environment: Azure, Oracle, Snowflake, Python, Active Batch, Informatica, IICS, Windows 11

Developed scalable Snowflake data pipelines for ingesting, transforming, and processing large volumes of structured and semi- structured data from multiple source systems.

Developed complex Snowflake SQL using CTEs, joins, window functions, MERGE statements, stored procedures, UDFs, and views to implement business transformation logic.

Implemented CDC and incremental data processing using Snowflake Streams, Tasks, Dynamic Tables, and SCD Type 1/Type 2 techniques.

Integrated Snowflake with Informatica IICS, AWS S3/Azure ADLS, and Databricks/PySpark to build end-to-end enterprise data engineering pipelines.

Optimized Snowflake performance and data quality through Query Profile analysis, SQL tuning, efficient warehouse utilization, reconciliation, auditing, and automated validation frameworks.

Administered and maintained enterprise Snowflake environments, including user provisioning, RBAC, security configurations, and platform governance to ensure high availability, stability, and operational excellence.

Optimized Snowflake warehouses, query performance, and workload management to maximize system efficiency, scalability, and application performance.

Designed and maintained enterprise Data Lake ingestion pipelines using cloud storage, enabling ingestion of structured, semi-structured, and flat-file data before transformation and loading into Snowflake for downstream analytics.

Monitored and optimized Snowflake credit consumption through resource governance, warehouse tuning, and cost optimization strategies to improve operational efficiency.

Implemented enterprise security controls, including RBAC, MFA, network policies, and data governance frameworks, ensuring compliance with organizational and regulatory standards.

Integrated Snowflake with cloud platforms and enterprise applications while automating deployments, data pipelines, and operational workflows using native Snowflake capabilities and cloud services.

Partnered with cross-functional stakeholders to drive Snowflake platform strategy, architecture, modernization initiatives, and continuous improvement aligned with business objectives and data transformation goals. BIOGEN, Cary, NC Duration: Apr 2024 – Apr 2025 Role: Data Engineer Lead Environment: AWS, Oracle, Snowflake, Databricks, Python, Active Batch, Informatica, IICS, Windows 11

Developed Data Lake ingestion frameworks leveraging AWS S3, Databricks, PySpark, and Snowflake to support scalable batch and real-time data processing using Medallion Architecture (Bronze, Silver, Gold).

Engineered and optimized scalable Snowflake data pipelines by leveraging micro-partitioning, clustering keys, and result caching to maximize query performance, improve workload efficiency, and reduce compute costs.

Developed high-performance data processing solutions using PySpark and Databricks SQL to streamline data cleansing, transformation, aggregation, and large-scale distributed data processing.

Designed and implemented robust ETL/ELT frameworks utilizing Snowflake Streams, Tasks, and Snowpipe to enable reliable, automated, and scalable real-time and batch data ingestion.

Automated and deployed ETL workflows to production through Databricks Jobs and CI/CD pipelines, ensuring streamlined release management, operational efficiency, and reliable production deployments.

Led end-to-end legacy data modernization and migration initiatives to Snowflake, ensuring seamless data migration, data integrity, minimal business disruption, and post-migration performance optimization. PARAMOUNT PICTURES, Los Angeles, CA Duration: Aug 2023 – Mar 2024 Role: Data Engineer Environment: AWS, Teradata 17.2, Oracle 19C, Snowflake, Airflow, Qlik 2022, Google Cloud BigQuery 1.28, Db2, Windows 11

Architected and led end-to-end Teradata to Snowflake migration initiatives, defining migration strategies, execution plans, and best practices to ensure seamless, scalable, and low-risk data transitions.

Conducted comprehensive data profiling, source system assessments, and migration feasibility analyses to identify dependencies, validate data quality, and ensure accurate and efficient data migration.

Optimized Snowflake performance by tuning SQL queries, configuring virtual warehouses, implementing clustering strategies, and optimizing storage to improve scalability, reduce query latency, and minimize operational costs.

Designed, developed, and optimized scalable ETL/ELT data pipelines in Snowflake, leveraging micro-partitioning, clustering keys, result caching, and Snowflake-native capabilities to maximize query performance, data processing efficiency, and cost optimization.

Collaborated with cross-functional teams to validate migration outcomes, perform reconciliation testing, resolve data quality issues, and ensure successful production deployment with minimal business disruption. PEARSON EDUCATION, Durham, NC Duration: Jan 2023 – Jul 2023 Role: Data Engineer Environment: AWS, Teradata 16.50, Oracle 19C, Snowflake, Airflow, Qlik 2022, Google Cloud BigQuery 1.28, Db2, Windows 11

Developed a comprehensive data mapping strategy to transform Teradata data types, schemas, constraints, and database objects into Snowflake-compatible structures, ensuring data integrity, consistency, and seamless migration.

Designed Data Lake ingestion and staging layers to support scalable ETL/ELT processing and high-performance analytics in Snowflake

Designed and implemented scalable Snowflake data models, including databases, schemas, tables, clustering keys, and partitioning strategies to optimize query performance, storage efficiency, and future scalability.

Performed detailed source-to-target mapping, data profiling, and impact analysis to validate data quality, resolve schema inconsistencies, and support a successful Teradata-to-Snowflake migration.

Defined and enforced Snowflake data architecture standards, including naming conventions, metadata management, and governance practices to improve maintainability, consistency, and compliance across enterprise data platforms.

Collaborated with architects, business stakeholders, and application teams to design high-performance Snowflake solutions aligned with business requirements, enabling efficient data ingestion, analytics, and reporting. JOHNSON & JOHNSON, Bridgewater, NJ Sep 2021-Dec 2022 Data Engineer

Developed scalable data pipelines in Databricks using PySpark, Delta Lake, and SQL for efficient data processing.

Ensured data integrity by managing DOMDs with defined relationships and transformations.

Structured and optimized Teradata schemas, tables, indexes, views, and other database objects to enhance performance and maintain data integrity. Mapped and documented data objects across systems for seamless integration.

Researched and recommended new data technologies, tools, and frameworks to enhance data infrastructure and efficiency.

Authored and fine-tuned complex Teradata SQL queries to maximize efficiency while minimizing resource consumption.

Enhanced query performance by leveraging Teradata’s indexing, partitioning, and join optimization techniques for faster data processing. Implemented data security measures and ensuring compliance with relevant data privacy regulations.

Identified and resolved performance bottlenecks through database tuning and query optimization strategies to improve overall system efficiency. Communicated findings, recommendations, and project updates to stakeholders and executives.

Monitored Teradata system performance, health, and risks to ensure high availability and minimal downtime.

Created & analyzed detailed reports on system performance, query execution times & error logs to drive continuous improvements.

STAPLES, Framingham, MA Feb 2021-Aug 2021 Database Engineer

Catalogued and documented all data sources, dependencies, and integration points to facilitate smooth migration and ensure business continuity.

Aligned Teradata data models with Snowflake schemas, ensuring accurate data transformation and minimizing migration risks. NYC HUMAN RESOURCES ADMINISTRATION, Brooklyn, NY Jan 2017-Jan 2021 Datawarehouse Architect

Designed and optimized ETL pipelines for seamless data extraction, transformation, and loading.

Integrated data from multiple sources (relational databases, cloud storage, APIs, flat files).

Ensured data accuracy, consistency, and transformation as per business requirements. JOHNSON & JOHNSON, New Brunswick, NJ Jul 2016-Dec 2016 ETL Developer

Performed data cleansing, manipulation, aggregation, and calculations to prepare data for loading into the data warehouse.

Built efficient loading procedures to transfer transformed data into the data warehouse, raising data integrity & performance.

Established data quality checks and validation procedures to ensure data accuracy and consistency.

PREVIOUS ROLES

Teradata Developer Albertsons Safeway, Phoenix, AZ Feb 2016-Jun 2016

Data Modeler T-Mobile, Seattle, WA Mar 2015-Jan 2016

ETL Developer Capgemini Client: UNILEVER, Hyderabad, India / New Jersey Nov 2014-Feb 2015

Software Consultant Tech Mahindra, Hyderabad Client: Barclays Oct 2010-Nov 2014

Software Developer - Mahindra Satyam Client: Vodafone Oct 2007-Sep 2010

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