Senior Data Engineer Azure, AWS, Snowflake SQL, ETL/ELT, Databrick

PRAMOD

Senior Data Engineer 407-***-**** ***********@*****.***

S U M M A R Y

I am a Senior Data Engineer with 10+ years of overall experience designing enterprise data platforms across finance, healthcare, retail, telecom, and manufacturing domains. I work extensively with SQL, ETL, SSIS, SSAS, SSRS, Azure, AWS, and GCP, building governed data lakes, OLTP and OLAP models, and scalable analytics solutions. My expertise includes cloud integrations, performance optimization, data quality enforcement, and compliance-ready reporting architectures.

Designing enterprise ETL pipelines using SQL and SSIS to integrate transactional and analytical datasets supporting governed ingestion and reliable enterprise reporting.

Implementing cloud-based data architectures on Azure AWS and GCP enabling scalable storage secure access and analytics readiness.

Developing complex SQL transformations to cleSanse normalize aggregate and validate large datasets for downstream analytical and regulatory reporting.

Building dimensional and relational data models supporting OLTP ingestion OLAP analytics and optimized query performance across enterprise platforms.

Orchestrating batch ETL workflows coordinating extraction transformation validation and load sequencing for scheduled enterprise processing cycles and compliance needs.

Engineering SSIS packages with robust error handling logging restartability and configuration driven execution across multiple environments enterprise deployments.

Designing SSAS tabular models enabling performant slicing dicing KPIs measures hierarchies and governed semantic layers for analytics consumption.

Creating SSRS reports and datasets delivering operational dashboards regulatory summaries and secure stakeholder access across enterprise domains teams.

Integrating hybrid data sources using SQL connectors managed identities secrets and resilient connectivity patterns across cloud platforms environments.

Implementing data quality frameworks enforcing validation rules reconciliation checks and anomaly detection across ETL pipelines enterprise-wide deployments.

Optimizing query performance through indexing partitioning aggregation strategies and execution plan tuning within SQL Server environments supporting analytics workloads.

Automating ETL deployments using scripted workflows ensuring version control repeatable releases and rollback support across enterprise environments consistently.

Monitoring data pipelines and analytics platforms to ensure availability performance stability and proactive issue resolution across production systems.

Establishing cloud cost governance by analyzing usage patterns budgets tagging policies and optimization recommendations across Azure AWS environments.

Supporting compliance readiness by delivering audit friendly reporting traceability controls and documented data lineage for regulated domains enterprise.

Collaborating with cross functional teams translating business requirements into scalable SQL based data products for enterprise analytics initiatives.

Documenting architectures mappings transformations and operational procedures ensuring maintainable and sustainable data platforms for long term operations support.

T E C H N I C A L S K I L L S

Databases & Querying: SQL Server, Azure SQL Database, MySQL, PostgreSQL, T-SQL

ETL & Data Integration: SSIS, Azure Data Factory, Informatica, Batch Processing

Analytics & BI: SSAS Tabular, SSRS, Power BI Report Server, Dimensional Modeling

Cloud Platforms: Azure, AWS, GCP

Data Storage: Azure Data Lake Gen1 Gen2, AWS S3, GCP Cloud Storage

Programming & Scripting: Python, PowerShell, Shell Scripting

Performance Optimization: Indexing, Partitioning, Query Tuning, Aggregation Design

Security & Governance: IAM, Encryption, Data Access Controls, Audit Readiness

DevOps & Version Control: Git, Azure DevOps, Jenkins

Methodologies: Agile Scrum, SDLC, Data Governance Standards E D U C A T I O N

B.Sc. in Computer Science,

Osmania University Hyderabad, India Jun 2015

E X P E R I E N C E

State of Colorado (DOF), Denver,CO Dec 2023 –Present Senior Data Engineer

Fiscal Data Platform: This project delivered a statewide financial data platform modernizing ingestion analytics and reporting through cloud and enterprise BI tooling. The solution implemented governed data lakes secure hybrid connectivity and standardized transformations with robust orchestration. Dimensional models semantic layers and automated deployments enabled reliable fiscal reporting audit readiness and operational transparency. Cloud services analytics engines and reporting frameworks were integrated to support scalable performance-controlled costs and long-term maintainability across finance operations.

Designed enterprise ETL pipelines using SQL transformations and SSIS packages to consolidate finance datasets ensuring governed ingestion reliability.

Implemented secure cloud data zones across AWS and Azure supporting scalable storage encryption identity access and cross region availability.

Developed analytical reporting models using SQL views and SSRS dashboards to deliver regulatory summaries and operational insights consistently.

Engineered multidimensional cubes using SSAS tabular models enabling performant slicing dicing calculations hierarchies and governed semantic layers enterprise.

Orchestrated batch ETL workflows coordinating source extraction transformations validations and load sequencing for scheduled fiscal processing cycles compliance.

Optimized data lake integrations on AWS improving throughput cost controls partition strategies lifecycle policies and downstream analytics readiness.

Configured enterprise networking and security baselines within Azure supporting private endpoints firewalls service endpoints and monitored connectivity operations.

Authored complex SQL procedures and SSIS control flows to enforce data quality rules error handling and restartable loads.

Built parameterized SSRS reports enabling ad hoc filtering drilldowns subscriptions and secure distribution to authorized finance stakeholders’ teams.

Designed enterprise semantic models through SSAS calculations KPIs measures perspectives and role-based security for analytics consumption platforms.

Automated ETL deployments using infrastructure scripts on AWS standardizing environments versioning rollback strategies and repeatable release processes governance.

Integrated hybrid data sources across Azure using SQL connectors managed identities secure secrets and resilient connectivity patterns architecture.

Implemented metadata driven ingestion frameworks with SSIS enhancing reusability configuration management logging auditing and operational transparency controls visibility.

Delivered compliance ready reporting through SSRS and ETL reconciliation checks supporting audits traceability approvals and evidence generation reviews.

Refined analytical schemas using SSAS and SQL to improve query performance aggregations caching strategies and calculation accuracy outcomes.

Established cloud cost governance across AWS and Azure implementing budgets tagging policies usage analytics and optimization recommendations controls.

Standardized data transformations through SSIS driven ETL patterns ensuring consistency reprocessing support error isolation and maintainability scalability longevity.

Validated reconciliations using SQL queries and SSRS outputs to confirm balances completeness variances and downstream reporting accuracy assurance.

Enhanced enterprise analytics adoption by tuning SSAS processing schedules memory settings aggregations and concurrency handling capacity planning governance.

Supported operational resilience on AWS by implementing backups recovery testing monitoring alerting and documented incident procedures runbooks playbooks.

Improved platform reliability within Azure through proactive monitoring capacity forecasting patch coordination and change management practices controls governance.

Collaborated with stakeholders translating financial requirements into SQL based data products documentation and sustainable delivery timelines expectations alignment. Environments: AWS S3, AWS Redshift, AWS IAM, Azure Data Factory, Azure Data Lake Gen2, Azure SQL Database, SQL Server 2019, SSIS 2017, SSRS 2017, SSAS Tabular 2017, Power BI Report Server, Windows Server 2019, Visual Studio 2019, Git, Azure DevOps, Terraform, Python 3.9, T-SQL, REST APIs, Active Directory, Kerberos, TLS 1.2, Agile Scrum CDC Foundation,Atlanta, GA Aug 2021 – Dec 2023

Data Engineer

Public Health Analytics: This project delivered a public health analytics platform supporting epidemiological reporting and program evaluation through modern data engineering practices. The solution integrated heterogeneous surveillance systems transactional sources and cloud storage using governed pipelines and semantic modeling. Batch processing analytical modeling and scalable reporting enabled evidence-based decision making. The platform combined enterprise databases cloud services and multidimensional analytics to support reliable trend analysis forecasting and transparent health outcome reporting.

Designed enterprise ETL workflows integrating OLTP health systems using SQL transformations and governed data validation logic across centralized analytical data stores.

Implemented SSIS packages to extract normalize and load surveillance datasets enabling consistent ETL execution schedules and resilient recovery handling mechanisms.

Developed analytical OLAP models using SSAS cubes to support dimensional slicing hierarchies, calculations and epidemiological trend analysis requirements.

Engineered cloud data pipelines across Azure and GCP supporting secure ingestion storage optimization and scalable analytics workloads enterprise wide.

Built normalized OLTP staging schemas using SQL to standardize source feeds prior to downstream ETL processing activities execution.

Optimized SSIS control flows and data flows improving ETL throughput logging reliability and restartable execution across multiple environments.

Designed SSAS tabular models aligning OLAP structures with public health indicators measures KPIs and standardized reporting requirements.

Integrated Azure services with on premises systems enabling hybrid ETL execution secure connectivity and centralized monitoring capabilities operations.

Authored complex SQL queries to reconcile OLTP source records against OLAP aggregates ensuring analytical accuracy and completeness consistently.

Implemented GCP based storage layers supporting scalable ETL ingestion archival policies and downstream analytics consumption patterns securely.

Delivered automated ETL scheduling using SSIS catalogs supporting dependency management alerts auditing and operational transparency needs enterprise wide.

Tuned SSAS processing strategies enhancing OLAP cube refresh performance aggregation efficiency and concurrent query responsiveness improvements.

Standardized SQL based data quality checks validating OLTP feeds anomalies outliers and transformation consistency across ETL cycles.

Collaborated with epidemiologists translating analytical requirements into SSAS dimensions measures hierarchies and OLAP calculations accurately.

Configured Azure security baselines for data pipelines supporting identity access management encryption and compliance controls implementation.

Established cross cloud data movement between Azure and GCP enabling resilient ETL workflows and regional availability support.

Refined OLTP extraction logic using SQL indexing partitioning and query optimization techniques improving upstream ETL stability.

Built monitoring dashboards tracking ETL execution SSIS failures SSAS processing status and data freshness metrics continuously.

Supported analytical research by modeling OLAP datasets enabling longitudinal analysis cohort comparisons and outcome evaluations effectively.

Automated validation scripts using SQL to compare ETL outputs against business rules and epidemiological benchmarks accurately.

Improved cloud cost governance across Azure and GCP by optimizing ETL compute utilization storage tiers and scheduling.

Documented ETL architectures SSIS mappings SSAS models and SQL logic ensuring knowledge transfer and maintainable operations.

Environments: SQL Server 2017, SSIS 2017, SSAS Tabular 2017, Azure Data Factory, Azure Data Lake Gen1, Azure SQL Database, GCP BigQuery, GCP Cloud Storage, Windows Server 2016, Visual Studio 2017, T-SQL, PowerShell, Python 3.7, Git, Azure DevOps, REST APIs, Active Directory, Kerberos, TLS 1.2, Agile Scrum

Home Depot, Austin, Texas Jun 2019 – Aug 2021

SQL Data Engineer

Retail Data Hub: This project delivered a centralized retail data engineering platform supporting merchandising supply chain and sales analytics. The solution consolidated transactional and operational datasets using structured pipelines relational databases and cloud services. Batch processing scalable storage and cross platform integrations enabled consistent reporting and analytics readiness. Multiple database engines ETL tooling and cloud infrastructure were combined to support performance optimization data reliability governance standards and enterprise-wide consumption for retail decision making.

Designed scalable ETL pipelines using SQL and SSIS to integrate retail transaction data from multiple operational systems secure enterprise platforms.

Implemented enterprise data ingestion workflows leveraging AWS and GCP to support secure storage processing and analytics scalability requirements across organizations.

Developed optimized SQL transformations for cleansing aggregating and standardizing datasets prior to downstream reporting consumption across enterprise analytics platforms environments.

Built relational schemas in SQLServer MYSQL and PostgreSQL to support transactional consistency analytical querying and historical tracking requirements enterprise wide.

Automated ETL execution using SSIS scheduling logging error handling and dependency management across retail data pipelines enterprise analytics environments systems.

Engineered hybrid data movement solutions between on premises SQLServer and cloud platforms AWS and GCP reliably for enterprise scale operations.

Optimized SQL queries for high volume retail datasets improving performance indexing partitioning and query execution efficiency across large scale systems.

Implemented SSIS data flows to consolidate sales inventory and pricing data into centralized analytical repositories supporting enterprise reporting needs initiatives.

Designed cloud storage layouts on AWS and GCP supporting ETL ingestion archival strategies and downstream analytics access patterns enterprise wide.

Standardized data models across MYSQL PostgreSQL and SQLServer enabling consistent reporting and cross system data reconciliation for enterprise analytics teams.

Developed reusable ETL components using SSIS improving maintainability configurability and scalability across multiple retail domains and business functions platforms enterprise.

Validated data accuracy using SQL reconciliation checks comparing ETL outputs against source system records regularly for audit readiness compliance assurance.

Integrated cloud-based analytics services with SQLServer sources enabling near real time reporting for retail stakeholders and leadership teams’ enterprise.

Improved ETL reliability by enhancing SSIS package performance monitoring logging alerts and restart capabilities across enterprise production environments systems platforms.

Collaborated with business teams translating retail requirements into SQL based data products and documentation supporting analytics delivery governance standards enterprise.

Tuned database performance across MYSQL PostgreSQL and SQLServer supporting concurrent workloads and analytical queries within enterprise retail platforms environments systems.

Implemented security controls across AWS GCP and database platforms ensuring data protection access governance compliance with enterprise standards policies frameworks.

Established backup recovery and retention strategies for SQLServer MYSQL and PostgreSQL environments supporting business continuity and disaster recovery objectives enterprise.

Built operational dashboards tracking ETL execution status SSIS failures and data freshness metrics for enterprise monitoring analytics platforms teams’ leadership.

Optimized cloud resource usage on AWS and GCP aligning ETL compute storage costs with workload demands and budgets enterprise wide.

Supported retail analytics initiatives by delivering trusted datasets through governed ETL and SQL modeling practices across enterprise reporting platforms teams.

Documented ETL architectures SSIS workflows database schemas and SQL standards ensuring long term maintainability for enterprise data platforms governance teams.

Environments: SQLServer 2016, SSIS 2016, MYSQL 5.7, PostgreSQL 10, AWS S3, AWS EC2, AWS IAM, GCP Cloud Storage, GCP Compute Engine, Windows Server 2016, Visual Studio 2017, T-SQL, PL/pgSQL, Bash, Python 3.6, Git, Jenkins, REST APIs, Active Directory, Agile Scrum

Charter Communications, Stamford, CT Nov 2017 – May 2019 ETL Data Engineer

Telecom Data Warehouse: This project delivered an enterprise telecom data warehousing solution supporting billing analytics operational reporting and performance insights. The platform unified transactional systems and analytical layers through structured ETL and ELT pipelines. Dimensional modeling reporting services and centralized management tools enabled consistent data consumption. OLTP integrations ROLAP query layers and MOLAP cubes supported scalable analytics. The solution improved data reliability reporting accuracy and operational transparency across large scale telecom environments.

Designed enterprise ETL and ELT pipelines using SQL and SSIS to ingest OLTP telecom billing datasets into analytical stores reliably.

Implemented ROLAP and MOLAP models enabling SSRS reporting performance analysis across finance operations using SQL aggregations and governed dimensions consistently.

Built ETL orchestration workflows through SSIS packages scheduled in SSMS ensuring OLTP extraction validation auditing and recoverable batch processing cycles.

Developed ELT transformations using SQL within cloud staging layers supporting ROLAP queries MOLAP refreshes and downstream SSRS consumption requirements enterprise.

Optimized SQL procedures and indexing strategies in SSMS improving OLTP load throughput ETL stability query plans and execution predictability platformwide.

Engineered ETL frameworks integrating source feeds via SSIS supporting telecom usage mediation billing reconciliation and governed dimensional modeling accuracy controls.

Designed MOLAP cubes and ROLAP views aligning OLTP measures hierarchies’ calculations and SQL semantics for enterprise SSRS dashboards delivery reliability.

Implemented ELT loading strategies leveraging SQL staging schemas improving ETL maintainability data lineage auditability and downstream analytics scalability outcomes consistently.

Built SSRS reports using SQL datasets sourced from ROLAP structures supporting operational monitoring trend analysis and executive telecom insights delivery.

Managed SSIS deployments through SSMS configuring parameters logging environments and versioned ETL executions across development testing and production landscapes securely.

Standardized OLTP extraction patterns using SQL ensuring ETL consistency data completeness reconciliation traceability and repeatable ingestion cycles enterprise-wide adoption.

Developed ELT based aggregation logic transforming raw SQL datasets into curated ROLAP and MOLAP layers supporting analytics accuracy governance requirements.

Tuned ETL performance by optimizing SSIS data flows memory usage buffering and parallelism across high volume telecom workloads reliably consistently.

Implemented SSRS security models with SQL role mappings ensuring controlled OLTP data visibility compliant access and audited reporting distribution enterprise.

Enhanced SSMS operational practices supporting ETL troubleshooting SQL debugging execution plan analysis and rapid production issue resolution workflows efficiency gains.

Integrated ELT pipelines with downstream ROLAP consumption layers enabling ad hoc SQL analytics and enterprise reporting scalability improvements delivery.

Validated ETL outputs using SQL reconciliation queries comparing OLTP records against MOLAP aggregates ensuring data accuracy completeness and trustworthiness standards.

Automated SSIS job monitoring alerts through SSMS enabling proactive ETL failure detection resolution and operational stability improvements platform wide.

Collaborated with analysts translating OLTP requirements into SQL driven ETL designs ROLAP models and SSRS deliverables effectively consistently aligned.

Documented ETL ELT architectures SSIS packages SQL standards ROLAP, MOLAP models and SSRS reporting guidelines for sustainable operations knowledge transfer.

Supported capacity planning by analyzing ETL runtimes SQL workloads OLTP growth patterns and MOLAP processing demands forecasting scalability needs proactively.

Improved data governance by enforcing ETL controls SQL validations SSIS standards and auditable OLTP to ROLAP data flows enterprise wide.

Environments: SQL Server 2014, SSIS 2014, SSRS 2014, SSMS 2014, Windows Server 2012 R2, T-SQL, OLTP Systems, ROLAP Models, MOLAP Cubes, Visual Studio 2013, PowerShell, Batch Scheduling, Data Warehousing, Dimensional Modeling, Star Schema, Indexing, Query Optimization, Git, Jenkins, Agile SDLC

Reliance Industries, Hyderabad, India Nov 2015 – Sep 2017 Junior Data Engineer

Manufacturing Data Platform: This project supported large scale manufacturing analytics by building foundational data engineering pipelines for production quality and operations reporting. The solution integrated plant systems sensor feeds and enterprise applications into centralized data stores. Batch processing structured transformations and relational modeling enabled consistent reporting and operational visibility. The platform emphasized data accuracy process traceability and scalable ingestion while supporting downstream analytics dashboards and management reporting across manufacturing units.

Designed foundational data pipelines to ingest manufacturing production records from plant systems into centralized databases supporting operational reporting needs.

Developed structured SQL queries to cleanse transform and aggregate raw production datasets supporting quality analysis throughput monitoring and reporting accuracy.

Assisted in building ETL workflows to extract transform and load shop floor data into enterprise reporting environments reliably.

Created normalized database schemas to store equipment production and inspection data ensuring referential integrity historical traceability and reporting consistency.

Supported batch data processing jobs by scheduling executions monitoring failures and validating successful loads across daily operational cycles.

Implemented data validation checks to identify missing records inconsistencies and anomalies within manufacturing source datasets proactively.

Collaborated with senior engineers to enhance ETL logic improving data completeness accuracy and downstream reporting usability outcomes.

Maintained SQL scripts for recurring data loads supporting production planning inventory tracking and operational performance monitoring needs.

Assisted in integrating sensor generated datasets with enterprise systems enabling consolidated manufacturing analytics reporting and decision support.

Optimized basic SQL queries by improving joins filtering conditions indexing strategies and execution performance across reporting workloads.

Supported reporting teams by preparing curated datasets for dashboards operational summaries and management review reports delivery.

Performed reconciliation activities comparing source system totals against loaded datasets ensuring consistency accuracy across reporting layers.

Documented data mappings transformation rules and database structures to support knowledge sharing onboarding and long-term maintenance activities.

Assisted with database maintenance tasks including backups data archival integrity checks and basic performance monitoring routines.

Participated in testing ETL jobs validating record counts transformations and successful completion prior to production releases deployment.

Supported issue resolution by analyzing data load failures reviewing logs and coordinating fixes with senior team members.

Maintained version-controlled SQL and ETL artifacts ensuring traceability-controlled changes and audit readiness across development cycles.

Helped implement access controls ensuring appropriate data visibility aligned with manufacturing operational roles and compliance policies.

Supported migration of legacy production datasets into standardized schemas improving long term analytics usability consistency and governance.

Coordinated with operations teams to understand data sources production workflows reporting expectations and integration requirements clearly.

Assisted in preparing monthly operational reports summarizing production volumes downtime trends quality indicators and performance insights.

Followed documented data engineering standards ensuring consistency reliability maintainability and scalability across manufacturing data solutions.

Environments: Oracle 11g, MySQL 5.6, SQL Server 2012, PL/SQL, T-SQL, Shell Scripting, Linux RHEL 6, Windows Server 2012, Informatica PowerCenter 9.6, ETL Pipelines, Batch Processing, Data Warehousing, Star Schema, Production Systems, Manufacturing MES, Git, SVN, TOAD, SQL Developer, Excel, Agile SDLC

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