Priyanka Tella
Dallas,TX
Summary
Results-driven Data Engineer with 7+ years in IT, specializing in enterprise data platforms, data warehousing, ETL/ELT pipelines, and cloud-native data solutions. Proven expertise in designing, building, and maintaining scalable, secure, and high-performance data pipelines supporting Enterprise Data Warehouses (EDW), Operational Data Stores (ODS), and Data Marts, particularly within financial services environments. Enterprise Data Engineering & Architecture: Strong experience designing and delivering enterprise-scale data integration frameworks across the data development lifecycle, ensuring data governance, security, privacy, and compliance. Data Warehousing & Cloud Platforms: Extensive hands-on experience with Snowflake, AWS, Azure, and large-scale data warehouse technologies including Oracle, Teradata, SQL Server, and cloud data lakes. ETL / ELT & Data Integration: Expertise in building high-volume batch and event-driven pipelines using IBM DataStage, PySpark, Python, SQL, AWS Glue, Azure Data Factory, Databricks, and modern ELT approaches. SQL & Performance Optimization: Advanced SQL development skills with deep expertise in performance tuning, optimized DDLs, query execution plans, partitioning, clustering, and large-scale data processing. Data Modeling & Design: Strong knowledge of logical and physical data modeling, including relational and dimensional modeling techniques to support analytical and reporting workloads. Cloud & Automation: Proven ability to architect and automate data solutions using AWS services (S3, EMR, Lambda, Step Functions, IAM, RDS, Athena, CloudWatch) and Azure services (ADF, ADLS, Synapse, Databricks), delivering resilient and future-ready platforms. Big Data & Distributed Processing: Hands-on experience with Apache Spark (Core, SQL, Streaming), Hadoop ecosystems, and large-scale distributed data processing frameworks. CI/CD & DevOps Practices: Proficient with Git-based version control and CI/CD pipelines using Jenkins, GitHub, Bitbucket, and Azure DevOps, enabling automated deployments and reliable releases. Workflow Orchestration & Monitoring: Experience implementing scheduled and trigger-based ingestion patterns using Airflow, Autosys with robust monitoring, alerting, and incident management. Agile Delivery & Collaboration: Strong experience working in Agile/Scrum environments, collaborating with Product Owners and global teams, supporting PI planning, requirement clarification, and on-time delivery. Leadership & Communication: Effective communicator with experience in design reviews (HLD/LLD), mentoring peers, production support, and driving continuous improvement across data platforms.
Results-driven data engineering professional with solid foundation in designing and maintaining scalable data systems. Expertise in developing efficient ETL processes and ensuring data accuracy, contributing to impactful business insights. Known for strong collaborative skills and ability to adapt to dynamic project requirements, delivering reliable and timely solutions.
Overview
7
7
years of professional experience
Work History
Data Engineer
Bank of America
Dallas, TX
02.2024 - Current
- Designed, built, and maintained enterprise-scale cloud data pipelines supporting Enterprise Data Warehouse and analytical workloads, ensuring data security, privacy, and governance for highly sensitive financial data.
- Delivered end-to-end SDLC in an Agile environment, collaborating with Product Owners and cross-functional teams during sprint planning, requirement clarification, and release coordination.
- Developed cloud-based ETL/ELT solutions using Python, PySpark, SQL, Snowflake, and AWS, supporting large-scale data ingestion, transformation, and analytics.
- Engineered high-volume PySpark pipelines on AWS EMR, leveraging Spark SQL and DataFrames to process multi-terabyte datasets stored in Amazon S3, Hive, and HDFS.
- Implemented automated ingestion and export frameworks for AWS S3 using Python, integrated with AWS Lambda and Step Functions, enabling both scheduled and event-driven ingestion patterns.
- Designed and optimized logical and physical data models in Snowflake, developing performance-optimized DDLs, stored procedures, and SQL queries to support downstream analytics and reporting.
- Performed SQL and pipeline performance tuning using partitioning, clustering, caching, and optimized execution plans, significantly improving query response times.
- Implemented federated analytics solutions using Presto (Starburst) across Snowflake and S3, reducing reporting latency by ~40%.
- Managed and secured AWS infrastructure (EC2, VPCs, IAM roles, security groups) to ensure secure, scalable, and compliant data processing environments.
- Built RESTful APIs using Python and PostgreSQL to enable secure, high-performance data access across enterprise applications.
- Automated machine learning pipelines using AWS Step Functions and SageMaker, supporting model training, deployment, and data publishing workflows.
- Created reusable PySpark frameworks and components, standardizing transformation logic and accelerating development across teams.
- Conducted unit testing, QA validation, and production support, resolving incidents, performing root cause analysis, and ensuring data accuracy and reliability.
Data Engineer
Wells Fargo
Dallas, TX
06.2023 - 01.2024
- Worked on the veteran’s project while dealing with most sensitive data in End-to-End business solutions.
- Designed and developed enterprise data ingestion and transformation pipelines using IBM DataStage and PySpark, integrating data from external sources and loading curated datasets into target systems.
- Developed and optimized complex SQL queries, translating business logic into scalable PySpark and Spark SQL implementations.
- Played a key role in legacy system modernization, migrating data processing workflows from SAS to Python, improving maintenance and performance.
- Utilized development and productivity tools including PyCharm, Jupyter Notebook, GitHub, SQL Developer, SecureCRT, and Linux environments.
- Investigated and resolved data quality and reporting issues by performing detailed root cause analysis and reconciling data discrepancies reported by business users.
- Built and optimized Spark applications for data validation, cleansing, aggregation, and analytical consumption, supporting data science and analytics teams.
- Implemented CI/CD pipelines using Jenkins and Git, enabling automated builds, testing, and deployments.
- Monitored and maintained production data pipelines using Apache Airflow and Linux bash scripting, reducing job failure rate by 30%.
- Automated batch workflows using Autosys, enabling reliable and repeatable execution of data processing jobs.
- Performed PySpark performance tuning, troubleshooting application failures and optimizing resource utilization for large-scale data workloads.
- Conducted data analysis and visualization using Python (Pandas, NumPy, Seaborn) and Tableau, supporting analytical insights and stakeholder reporting.
- Worked collaboratively in Agile teams, supporting sprint execution, production support, and continuous improvement initiatives.
Data Engineer
Optum
Hyderabad, IN
04.2020 - 11.2022
- Designed and implemented enterprise data integration pipelines using Azure Data Factory (ADF) to ingest data from on-premises and cloud source systems into Azure Data Lake Storage (ADLS).
- Created and managed ADF Linked Services and Pipelines for sources including Azure SQL Server, Blob Storage, REST APIs, implementing robust error handling and recovery frameworks.
- Built dynamic and parameterized ADF pipelines to support multiple sources and targets, leveraging Azure Key Vault for secure credential management.
- Implemented scheduled and trigger-based ingestion patterns, monitored pipeline executions, and configured alerts for operational visibility.
- Developed Slowly Changing Dimension (SCD-1 and SCD-2) frameworks using Azure Databricks and Spark, supporting enterprise data warehouse use cases.
- Migrated data from on-prem SQL Server to Azure Synapse Analytics (DW) and Azure SQL Database, supporting cloud modernization initiatives.
- Developed Spark (Scala and Python) notebooks to transform, partition, and optimize data stored in ADLS.
- Integrated Azure Databricks notebooks with ADF pipelines, passing runtime parameters using Databricks widgets.
- Implemented real-time data ingestion using Azure Stream Analytics to load streaming data into Azure SQL Data Warehouse.
- Built logging, auditing, and monitoring frameworks for ADF pipelines, ensuring traceability and operational resilience.
- Implemented CI/CD pipelines using Azure DevOps (VSTS), including ARM templates, PowerShell scripts, and parameterized deployment artifacts.
- Conducted design reviews, enforcing reference architecture, naming standards, and best practices across data ingestion solutions.
- Collaborated with global teams to support delivery, optimization, and continuous improvement of enterprise data platforms.
Software Engineer
Kastech
Hyderabad, IN
03.2017 - 03.2020
- Worked on building centralized Data Lake on AWS Cloud utilizing primary services like S3, EMR, Redshift and Athena and Glue.
- Hands on Experience in migrating datasets and ETL workloads with Python from On-prem to AWS Cloud services.
- Built series of Spark Applications and Hive scripts to produce various analytical datasets needed for digital marketing teams.
- Worked extensively on building and automating data ingestion pipelines and moving terabytes of data from existing data warehouses to cloud.
- Worked extensively on fine tuning spark applications and providing production support to various pipelines running in production.
- Worked closely with business teams and data science teams and ensured all the requirements are translated accurately into our data pipelines.
- Worked on full spectrum of data engineering pipelines: data ingestion, data transformations and data analysis/consumption.
- Developed AWS lambdas using Python and Step functions to orchestrate data pipelines.
- Worked on automating the infrastructure setup, launching and termination EMR clusters etc.
- Created Hive external tables on top of datasets loaded in S3 buckets and created various hive scripts to produce series of aggregated datasets for downstream analysis.
- Build real time streaming pipeline utilizing Kafka, Spark Streaming and Redshift.
- Worked on creating Kafka producers using Kafka Java Producer API for connecting to external Rest live stream application and producing messages to Kafka topic.
- Implemented a Continuous Delivery pipeline with Bitbucket and AWS AMI's.
- Designed, documented operational problems by following standards and procedures using Jira.
Education
Bachelors - Computer Science
Acharya Nagarjuna University (ANU)
A.P, IN06-2016
Skills
- AWS
- AZURE
- Python
- Snowflake
- Hadoop
- HDFS
- Hive
- Spark
- Kafka
- SQL
- PySpark
- Pandas
- Jenkins
- GitHub
Timeline
Data Engineer
Bank of America
02.2024 - Current
Data Engineer
Wells Fargo
06.2023 - 01.2024
Data Engineer
Optum
04.2020 - 11.2022
Software Engineer
Kastech
03.2017 - 03.2020
Bachelors - Computer Science
Acharya Nagarjuna University (ANU)