Hema C

Data Engineer

North Brunswick, New Jersey, United States0 mo experience

Key Highlights

8+ years of experience in data engineering.
Expert in building scalable data platforms.
Proven track record in fraud detection and analytics.

Stackforce AI infers this person is a Data Engineering expert in Fintech and Healthcare sectors.

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Skills

Core Skills

Cloud Data PlatformsEtl/elt DevelopmentBig Data TechnologiesData Visualization

Other Skills

AWS EMRAWS KinesisAmazon Web Services (AWS)Apache AirflowApache KafkaApache SparkAzure Data FactoryAzure DatabricksAzure DevOpsAzure FunctionsAzure SynapseDeep LearningExtractGitGitLab

About

Senior Data Engineer with 8+ years of experience building scalable data platforms and analytics solutions across healthcare, financial services, and technology sectors. Specialized in architecting end-to-end data pipelines using modern cloud technologies including Snowflake, Azure, and AWS. Core Expertise: • Cloud Data Platforms: Snowflake, Azure Synapse, AWS EMR, Redshift • Big Data Technologies: Apache Spark, PySpark, Kafka, Airflow • ETL/ELT Development: Azure Data Factory, AWS Glue, Informatica • Programming: Python, SQL, SnowSQL • Data Visualization: Power BI, Tableau • DevOps: Azure DevOps, GitLab CI/CD, JIRA Currently at Elevance Health, I design and implement enterprise-grade data solutions that drive business intelligence and analytics. My work spans from architecting multi-cloud Snowflake environments to developing real-time data pipelines that process millions of records daily. Previously at Mastercard, I built fraud detection pipelines and migrated legacy systems to modern cloud-based architectures, improving processing efficiency by 60%. Passionate about data engineering innovation, I continuously explore emerging technologies and best practices to deliver robust, scalable solutions that enable data-driven decision making.

Experience

Elevance health

Sr. Data Engineer

Sep 2023 – Present · 2 yrs 6 mos · New Jersey, United States · Hybrid

Collaborated with cross-functional stakeholders to gather business requirements and translate them into scalable data engineering solutions
Designed and developed ETL/ELT pipelines using Azure Synapse (SQL & Spark Pools) and PySpark from Oracle, SQL Server, and other sources
Built large-scale data ingestion workflows on AWS EMR, orchestrating data movement across S3, DynamoDB, and analytics platforms
Implemented CI/CD pipelines using Azure DevOps for streamlined deployment and version control
Configured secure multi-cloud Snowflake environments on AWS and Azure with performance optimization
Utilized Snowflake features: Resource Monitors, RBAC, Data Sharing, Virtual Warehouse tuning, Snowpipe, Streams, Tasks, Zero-Copy Cloning
Designed optimized dimensional data models in Snowflake to improve query performance and storage efficiency
Integrated Apache Airflow with AWS to orchestrate ML and ETL workflows including Amazon SageMaker models
Developed high-performance Spark applications reducing query latency and improving throughput
Led POCs for emerging data engineering tools and built production-ready Python pipelines
Built data transformation pipelines using AWS Glue with DynamicFrames, Crawlers, and Catalog integration

Azure SynapsePySparkAWS EMRSnowflakeAzure DevOpsApache Airflow+2

Mastercard

Sr. Data Engineer

Jul 2019 – Aug 2023 · 4 yrs 1 mo · St Louis, Missouri, United States · Remote

Developed scalable ETL frameworks using Apache Spark, Python, Azure Databricks, and Snowflake for high-volume transaction processing and fraud analytics
Migrated legacy SAS workflows to Python and Spark-based pipelines on Azure Databricks, improving performance and maintainability
Integrated datasets from external APIs and Azure storage using Azure Data Factory, Apache NiFi, and Python ingestion scripts
Built and maintained CI/CD pipelines in GitLab, automating deployment of Spark jobs and Python modules across Azure environments
Implemented RBAC, encryption-at-rest, and secure data access policies in Snowflake on Azure, ensuring PCI DSS compliance
Enforced data quality and validation using the Great Expectations framework, improving trust in fraud and transaction datasets
Designed Kafka-based event-driven pipelines for real-time fraud alerting, enabling faster anomaly detection
Collaborated with fraud analytics teams to surface actionable insights from transaction patterns
Automated ETL workflows using Azure Functions, reducing manual intervention and improving pipeline efficiency
Utilized Azure Databricks, Spark, Hive, and Azure Synapse Analytics to process large-scale financial datasets
Built data integration pipelines using Informatica, Sqoop, and big data tools for seamless movement between on-prem and Azure platforms

Apache SparkPythonAzure DatabricksSnowflakeAzure Data FactoryGitLab+2

Pioneer labs

Data Engineer

Mar 2017 – Dec 2018 · 1 yr 9 mos · Hyderabad, Telangana, India

Designed and deployed cloud-based BI dashboards using Power BI and Tableau on Azure and AWS for network performance monitoring and R&D analytics
Optimized SQL queries, star-schema models, and analytical datasets in Amazon Redshift and Azure Synapse to enable low-latency reporting on network traffic logs
Led automation efforts using Python, AWS Lambda, and Azure Functions to streamline reporting workflows and dashboard updates
Developed near-real-time data pipelines using AWS Kinesis, Azure Event Hubs, and Spark Streaming to ingest and process network traffic data, improving system uptime by 25%
Built network traffic analysis dashboards using data stored in Amazon S3 and Azure Data Lake Storage
Automated data ingestion from SNMP traps, syslogs, and network monitoring APIs using AWS Glue and Apache Airflow
Reduced cloud storage expenses by 30% through data partitioning, archival strategies, and lifecycle management in S3 and Azure Blob Storage
Integrated network logs, third-party REST APIs, and telemetry feeds into centralized reporting repositories