Data Engineer - SQL Developer with 5+ Years

HIMABINDHU K

Data Engineer / SQL Developer

(***) 745- 6588 *************@*****.***

• Open to Relocation

Professional Summary

SQL Developer / Data Engineer with 5+ years of experience specializing in advanced SQL development, data modeling, and performance optimization for large-scale enterprise and healthcare data platforms. Expert in designing complex T-SQL solutions, including stored procedures, views, functions, and analytical queries that support high- volume reporting and business intelligence workloads.

Strong background in building and optimizing relational and analytical data models, including star and snowflake schemas, fact and dimension tables, and Slowly Changing Dimensions (SCD Type 1 & 2). Proven ability to analyze execution plans, implement indexing strategies, refactor inefficient queries, and significantly improve query performance and reporting response times.

Experienced in developing SQL-driven ELT frameworks integrated with modern cloud platforms such as Azure SQL Database, Azure Synapse Analytics, Snowflake, and Databricks. Adept at partnering with data engineers, BI developers, and business stakeholders to translate requirements into efficient, scalable, and maintainable SQL solutions.

Recognized for strong ownership, attention to data accuracy, and commitment to data quality, governance, and compliance (HIPAA). Known for mentoring junior developers, enforcing SQL best practices, and delivering reliable datasets that enable analytics, dashboards, and data-driven decision making.

Technical Skills

Skill Area

Technologies & Expertise

SQL & Data Modeling

Advanced T-SQL, complex joins, CTEs, window functions, query optimization, indexing strategies, execution plan analysis, partitioning, star & snowflake schemas, SCD Type 1 & 2

Cloud & Data Platforms

Azure Data Factory (ADF v2), Azure Synapse Analytics, Azure SQL Database, Azure Data Lake Gen2, Azure Fabric, Snowflake, Databricks, Teradata

ETL / ELT &

Architecture

Metadata-driven pipelines, Medallion architecture, CDC, incremental & historical loads, data marts, Lakehouse design .

Design Big Data & Processing

Apache Spark, PySpark, Spark SQL, Hadoop, Hive

Streaming & Real- Time

Kafka, Azure Event Hubs, Spark Structured Streaming

Programming & Analytics

SQL, T-SQL, Python, PySpark, DAX, PL/SQL

BI & Visualization

Power BI (Desktop & Service), Tableau, SSRS, semantic modeling, RLS, advanced DAX

Orchestration & DevOps

Azure Data Factory, Apache Airflow, Git, GitHub, Azure DevOps, CI/CD pipelines

Security, Quality & Governance

RBAC, Managed Identities, Azure Key Vault, data validation frameworks, reconciliation checks, audit logging, HIPAA- compliant data handling

Containers & CloudOps

Docker, Kubernetes (AKS/EKS), Istio Service Mesh

Professional Experience

Index Analytics - Windsor Mill, MD, 21244 May 2025 – Present SQL Developer / Data Engineer

Designed and developed end-to-end SQL-based ETL/ELT pipelines for large-scale

healthcare claims data, supporting both analytical and operational reporting.

Built and maintained staging, temporary, and target tables to enable complex transformations, incremental loads, and historical processing.

Developed highly optimized SQL queries using complex joins, subqueries, CTEs, window functions, QUALIFY clauses, and aggregations for high-volume datasets.

Led development of historical restatement workflows to reprocess legacy healthcare claims data across multiple AC versions, ensuring accurate correction of impacted claim header and claim line record

Implemented duplicate detection and resolution logic using composite business keys, ROW_NUMBER, batch identifiers, and reconciliation techniques to prevent double- counting.

Designed and enforced data quality and reconciliation frameworks, including validation rules, audit tables, exception reporting, and count reconciliation.

Worked extensively with healthcare claims domain data including CMS, FISS, MRUR, and NCH datasets, ensuring adherence to healthcare business rules and reporting standards.

Developed and executed AWS Glue Python Shell jobs with runtime parameters for dynamic ETL orchestration and integrated Glue jobs with AWS S3 for SQL scripts and control files.

Migrated legacy DEI Python and shell-based workflows to cloud-native AWS Glue and SQL-driven implementations, improving scalability and maintainability.

Built and managed Snowflake staging, temporary, and target tables, writing complex multi-join SQL transformations and performing performance tuning on large datasets.

Performed extensive data profiling, null analysis, source-to-target validation, and reconciliation to support business reviews and stakeholder sign-off.

Collaborated closely with Business Analysts, Architects, and Product Owners in Agile environments to translate requirements into scalable, production-ready data solutions.

Participated in code reviews, enforced SQL and ETL best practices, and contributed to continuous process improvements.

Environment: SQL Server, Snowflake, AWS Glue, AWS S3, SQL, Python, Azure Data Factory, Power BI, Git.

UnitedHealth Group – Horsham, PA July 2022 – April 2025 Data Engineer

Designed, developed, and maintained enterprise-scale data warehouses and analytical data stores using Azure Synapse, Azure SQL Database, and ADLS Gen2.

Built and optimized ADF v2 pipelines for batch and near–real-time ingestion using parameterization, triggers, reusable frameworks, and metadata-driven designs.

Developed complex T-SQL stored procedures, views, and functions to implement healthcare business logic and improve reporting efficiency.

Performed extensive SQL performance tuning, including indexing strategies, partitioning, execution plan analysis, and query refactoring.

Led migration of legacy SSIS-based ETL to modern Python, Spark, and Databricks

workflows, improving scalability and reducing operational overhead.

Implemented Slowly Changing Dimensions (SCD Type 1 & 2) and fact tables using star schema modeling.

Designed and enforced data quality frameworks with validation rules, reconciliation checks, and audit logging.

Integrated Kafka and Azure Event Hub for real-time data ingestion pipelines.

Collaborated with data scientists to operationalize machine learning models, embedding Python scripts into production pipelines.

Designed optimized Power BI semantic models with advanced DAX, RLS, and incremental refresh.

Created reusable ingestion frameworks for SFTP, APIs, and relational sources using ADF and Databricks.

Worked closely with business stakeholders to translate requirements into scalable data solutions.

Mentored junior engineers and led best practices for SQL, Spark, and pipeline optimization.

Ensured compliance with HIPAA and healthcare data standards.

Environment: Azure Data Factory, Databricks, ADLS Gen2, Azure Synapse, SQL Server, PowerBI,Python,PySpark,Kafka,Snowflake,AWSServices

CGI (Remote) Nov 2019 – July 2021

Data Engineer

Designed and implemented ADF v2 pipelines to ingest data from relational databases, flat files, APIs, and cloud storage.

Developed and maintained SSIS packages for enterprise ETL workflows, including error handling and automated scheduling.

Wrote and optimized complex SQL queries, stored procedures, and views to support reporting and analytics.

Performed SQL Server tuning, including indexing, query optimization, and resource monitoring using SQL Profiler.

Built PySpark applications to process large volumes of structured and semi-structured data in Databricks.

Implemented Snowflake Snowpipe for continuous data ingestion and improved data freshness.

Designed and deployed Power BI dashboards with secure Row-Level Security (RLS).

Supported both batch and streaming solutions using ADF, Databricks, and Azure Stream Analytics.

Migrated SQL Server databases and upgraded SSIS packages across versions.

Collaborated with business analysts to understand requirements and translate them into technical solutions.

Implemented data validation and reconciliation checks to ensure data accuracy and reliability.

Deployed data workloads as Kubernetes pods, ensuring high availability and fault tolerance.

Documented ETL workflows, data models, and operational procedures.

Environment: Azure Cloud, ADF v2, Databricks, Azure SQL, Snowflake, Power BI, Python, PySpark, Kubernetes .

Education

Master of Science in Computer Science - Franklin University - Columbus, OH.

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