Data Engineer Power Bi

SASANK k

Data Engineer

***********@*****.*** TX +1-682-***-**** https://www.linkedin.com/in/sasank-ko

PROFESSIONAL SUMMARY

Experienced Data Engineer with 5+ years of expertise in designing and implementing scalable, cloud-native data solutions across healthcare, finance, logistics, and retail. Proficient in building real-time and batch data pipelines, data migration, and integration using Spark, PySpark, Snowflake, and cloud platforms (AWS, Azure, GCP). Skilled in orchestrating ETL workflows with Airflow, DBT, and Informatica while ensuring data quality, governance, and compliance (HIPAA, GDPR). Adept at performance tuning, cost optimization, and automating CI/CD deployments using Terraform, Jenkins, and GitHub Actions. Strong background in analytics and predictive modelling, enabling insights that improve operational efficiency and business outcomes. Proven ability to mentor teams, collaborate cross-functionally, and deliver data platforms that support BI and ML workloads.

TECHNICAL SKILLS

•Programming Languages: Python, R, SQL, SAS, Shell Scripting

•Big Data & ETL: Apache Spark, PySpark, Kafka, Airflow, DBT, Informatica PowerCenter, Talend, AWS Glue, SSIS, ADF

•Cloud Platforms: AWS (S3, Glue, Redshift, Lambda, EMR, EKS), Azure (Data Factory, Databricks, Synapse), GCP (Big Query,

•Dataflow, Pub/Sub, Cloud Functions)

•Databases/Data Warehousing: Snowflake, Redshift, SQL Server, Oracle, PostgreSQL, MySQL, MongoDB, Cassandra

•DevOps & CI/CD: Git, GitHub, GitLab, Jenkins, Terraform, Azure DevOps, Bitbucket

•Data Visualization: Tableau, Power BI, Advanced Excel

•Machine Learning/Analytics: NumPy, Pandas, Scikit-Learn, TensorFlow, Clustering, Classification, Hypothesis Testing, Time Series

•Other Tools: Erwin, MS Project, Control-M, UNIX Shell Scripting

EDUCATION

Master of Science in Computer Science University of Texas Aug 2021 - May 2023

PROJECTS & PROFESSIONAL EXPERIENCE

Projects

Logistics and Courier Services – Data Management & Analytics

•Performed comprehensive data analysis for a fictitious courier and logistics company to streamline delivery processes and reduce turnaround time.

•Collected, cleaned, and structured delivery transaction data, including package IDs, delivery times, route codes, and service delays.

•Designed a relational database schema to improve tracking and warehouse efficiency, supporting multi-hub data storage, retrieval, and updates.

•Conducted geospatial and time-based analyses of delivery delays, route overlaps, and service inconsistencies across three main regions using SQL.

•Developed interactive Power BI and Tableau dashboards to visualize hub-level performance, truck utilization, delivery metrics, and route efficiency.

Heart Disease Detection – Predictive Analytics Using Data Mining

•Created a predictive model to assess heart disease risk using patient characteristics such as age, blood pressure, cholesterol, and ECG readings.

•Applied classification algorithms including Logistic Regression, Decision Tree, KNN, Naïve Bayes, and Random Forest, achieving up to 92% prediction accuracy after hyperparameter tuning and feature selection.

•Evaluated model performance using precision, recall, F1 score, and ROC-AUC to ensure clinical-grade sensitivity and reliability.

Professional Experience & Skills

•Strong experience in Software Development Life Cycle (SDLC) including Requirements Analysis, Design Specification, and Testing across Waterfall and Agile methodologies.

•Adept at working in multi-cloud environments (AWS, Azure, GCP), designing cloud-native data solutions optimized for cost, performance, and scalability.

•Built and maintained batch and real-time data pipelines using Apache Spark, PySpark, Kafka, Airflow, and DBT, handling structured, semi-structured, and streaming data.

•Expertise in data warehousing & analytics platforms including Snowflake, BigQuery, and Redshift, with experience in building data models, performance tuning, and data governance.

•Led cloud migration initiatives involving legacy systems, orchestrating data movement, schema transformation, and validation with zero data loss and minimal downtime.

•Developed modular, production-grade ETL pipelines integrating data from APIs, IoT devices, flat files, and relational databases using AWS Glue and Azure Data Factory.

•Built audit frameworks, data quality validation layers, and wrote reusable unit tests with Pytest to ensure pipeline robustness and SLA compliance.

•Managed CI/CD pipelines and Infrastructure as Code (IaC) using GitHub Actions, Terraform, and Azure DevOps for automated deployments and consistent environments.

•Collaborated with business analysts, DevOps engineers, and product managers to translate data needs into scalable solutions supporting BI and ML workloads.

•Built and published interactive dashboards in Power BI and Tableau to visualize operational metrics, pipeline performance, and business KPIs in real-time.

•Mentored junior engineers, conducted internal workshops on DBT, Snowflake, and Spark, and contributed to peer reviews and documentation to raise team technical standards.

•Continuously driven by a passion for data innovation, delivering clean, reliable, and governed datasets that empower decision-makers and improve business outcomes.

Role: Data Engineer May 2023 – Present

Thrivent TX May 2023 – Present

Responsibilities:

•Designed and developed scalable ETL pipelines using AWS Glue and Snowflake to streamline ingestion of complex healthcare and financial datasets, reducing processing time by 40%.

•Built real-time streaming ingestion pipelines from Kafka event hubs into PySpark, enabling fraud detection and real-time analytics.

•Architected a Delta Lake-based storage system in Azure Databricks with ACID transactions, schema enforcement, and rollback capabilities.

•Automated API ingestion workflows with Azure Logic Apps and integrated with Snowflake to support compliance and regulatory reporting.

•Deployed DBT models (staging, intermediate, mart layers) with test coverage, Jinja macros, and reusable configurations to standardize transformations.

•Orchestrated Airflow DAGs with SLA monitoring, retries, and automated alerts via Slack and email for proactive pipeline monitoring.

•Developed Power BI and Tableau dashboards to track KPIs, pipeline SLAs, and executive-level insights, ensuring transparency across teams.

•Partnered with DevOps to implement CI/CD pipelines using GitHub Actions and Terraform for automated, multi-environment deployments.

•Implemented role-based access controls (RBAC) and data masking policies in Snowflake to ensure compliance with internal governance frameworks.

•Mentored junior engineers and created internal playbooks for Snowflake, DBT, and Spark, supporting team skill development and knowledge sharing.

Role: Data Engineer

Cigna TX Jan 2022 – Apr 2023

Responsibilities:

•Migrated on-prem SQL Server data marts to AWS Redshift and Snowflake, optimizing queries and reducing runtime by 35%.

•Automated data ingestion pipelines with AWS Glue and Python, cutting manual preparation efforts by 40%.

•Built and deployed Power BI dashboards for executives to monitor revenue patterns, lending performance, and operational KPIs.

•Designed and deployed Java Spring Boot microservices integrated with APIs and ETL pipelines to support customer segmentation, credit risk scoring, and financial reporting.

•Implemented machine learning models in Python for customer behavior prediction, boosting marketing response rates by 20%.

•Enforced role-based access control (RBAC) across AWS and Snowflake to ensure compliance with governance policies.

•Enhanced CI/CD pipelines with Jenkins and GitHub, reducing deployment errors by 30% and improving release reliability.

•Collaborated with business analysts and QA teams in Agile sprints, achieving a 100% on-time delivery rate for high-priority data pipelines and dashboards.

•Optimized ETL workflows using Informatica PowerCenter and SQL tuning, improving throughput and reducing data pipeline latency by 25%.

•Partnered with cloud engineers to implement secure data encryption and key management, strengthening compliance with HIPAA and internal security standards.

Role: Data Engineer

Infor India Nov 2019 – Jul 2021

Responsibilities:

•Led a hybrid onsite–offshore team and improved delivery timelines for reporting solutions by 20% through coordinated Agile sprint planning using JIRA.

•Designed and delivered advanced Power BI and Tableau dashboards to track sales trends, customer sentiment, and warranty claims, driving 20% improvement in data-driven decision-making.

•Built complex Tableau dashboards incorporating YoY, QoQ, MTD, YTD trends, LOD expressions, and row-level security, enhancing leadership visibility across business units.

•Improved dashboard performance and security by 35% by publishing and managing Tableau Online with user-specific access controls.

•Developed and optimized materialized views and SQL queries across PostgreSQL, Snowflake, Oracle, and MicroStrategy, achieving a 30% reduction in query execution time and faster stakeholder access to critical business data.

•Supported KPI reporting with 98%+ accuracy by reconciling data across Oracle, SQL Server, and AS400 platforms.

•Automated ETL pipelines using Control-M and UNIX shell scripting, improving reliability and cutting manual intervention by 40%.

•Built reusable Python and SQL scripts to automate reporting workflows, reducing processing time by 35% and accelerating decision turnaround for business users.

•Implemented AI-driven predictive analytics on IoT telemetry data, improving device performance monitoring and reducing product defects by 15%.

•Maintained data governance and version control best practices to ensure compliance, consistency, and reliability across ETL and reporting environments.

•Participated in Agile sprint reviews and issue tracking, increasing dashboard release velocity by 15%.

•Delivered ad hoc analysis and statistical reporting with SAS to measure marketing effectiveness and operational KPIs.

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