I am a skilled BI Developer and Data Engineer with 8 years of experience in data analysis and value extraction. My work focuses on designing and orchestrating modern data pipelines, transforming data into actionable insights for businesses. I specialize in Power BI, SQL, and have advanced in data engineering with recent training in MLOps, covering tools like Airflow, dbt, and platforms such as Snowflake and Databricks. My approach blends hands-on industrial experience with modern data architecture practices, enabling me to create reliable data models and efficient workflows. If you need a data expert who can elevate your analytics capabilities and drive informed decision-making, let’s discuss how I can contribute to your project.

I help companies design, build, and modernize scalable data platforms that turn raw data into reliable analytics, automation, and AI-ready products. I’m a Senior Data Engineer / Cloud Data Engineer with 9+ years of experience building production-grade data pipelines, lakehouse platforms, ETL/ELT systems, cloud data warehouses, real-time streaming pipelines, and analytics-ready data models across AWS, Azure, GCP, Databricks, Snowflake, Redshift, BigQuery, SQL Server, and modern AI/GenAI workflows. My strongest work is in: • Cloud Data Engineering: AWS Glue, Lambda, S3, Redshift, Athena, EMR, Step Functions, Azure Databricks, ADLS, ADF, Synapse, GCP BigQuery, Dataflow, Pub/Sub • Databricks & Lakehouse: PySpark, Delta Lake, Unity Catalog, Medallion Architecture, Bronze/Silver/Gold pipelines, performance tuning, partitioning, Z-Ordering • ETL / ELT Pipeline Development: Python, SQL, PySpark, dbt, Airflow, Dagster, Spark, Kafka, APIs, batch and near-real-time ingestion • Data Warehouse Modernization: SQL Server, SSIS, Oracle, legacy ETL migration to Databricks, Snowflake, Redshift, and BigQuery • Data Modeling & Analytics: dimensional modeling, star schema, fact/dimension tables, semantic layers, Power BI, Tableau, Domo, QuickSight • Data Quality & Governance: data validation, reconciliation, monitoring, lineage, metadata, access control, CI/CD, automated testing, and production support • GenAI / AI Data Platforms: RAG pipelines, vector search, embeddings, LangChain, LangGraph, LLM workflows, AI-ready datasets, and enterprise knowledge platforms I specialize in solving messy, real-world data problems: broken pipelines, slow SQL, unreliable dashboards, disconnected systems, manual reporting, poor data quality, and cloud migration challenges. I can take a project from requirements gathering to architecture, development, testing, deployment, documentation, and long-term support. What clients can expect from me: • Clean, maintainable, production-ready code • Strong communication and clear project ownership • Scalable cloud architecture designed for performance and cost efficiency • Reliable pipelines with monitoring, error handling, and documentation • Business-focused delivery, not just technical implementation • Fast ramp-up on existing systems, data models, and business logic I have worked across banking, aviation, energy, SaaS, analytics, and enterprise data platform environments, supporting teams with data migrations, reporting modernization, cloud warehouse builds, Databricks lakehouse development, Domo to Redshift migration, SQL Server to Databricks migration, real-time streaming pipelines, and AI/ML data infrastructure. If you need a data engineer who can own the work end-to-end, communicate clearly, and deliver reliable systems that your business can trust, I’d be happy to help.

I am a seasoned Data Engineer with over 17 years of experience in building scalable ETL solutions and designing efficient data Engineering and architectures solution. >> 17+ years of experience in Data Engineering and ETL development >> Strong expertise in building scalable and efficient data architectures >> Advanced proficiency in Snowflake (data warehousing, schema design, performance tuning, query optimization) >> Expertise in SQL for complex data transformations and analytics >> Hands-on experience with dbt for modular, reusable, and maintainable data models >> Skilled in Python for data processing and automation >> Experience with Apache Airflow for workflow orchestration, scheduling, and dependency management >> Strong knowledge of cloud-based data solutions and modern data stack >> Proven ability to design and implement scalable ELT pipelines >> Experience in data modeling (dimensional, star, and snowflake schemas) >> Strong focus on data quality, validation, and governance frameworks >> Ability to transform complex data into actionable business insights >> Extensive experience with Informatica PowerCenter (ETL development, workflows, mappings, performance tuning)

I build production-grade data & AI systems that turn messy sources into reliable, decision-ready outputs. From marketing mix modeling to Bayesian A/B testing and LLM products, I have shipped many working products. What I Deliver: ✅ Warehouses & pipelines (BigQuery/Postgres) with clean schemas, tests, monitoring ✅ Marketing analytics: unite all sources to create dashboards that inform ✅ Experimentation: Bayesian A/B framework, uplift analysis, forecasting ✅ LLM services: FastAPI endpoints for RAG/structured outputs, evals, cost controls Stack I Use: → Python, SQL, AWS, Postgres, Power BI, Streamlit → FastAPI, LLMs, Modal, Airflow, Jenkins, Git, Docker Why makes me different: → Not just product building - clear communication. Proven experience dealing with customers. → Operator mindset: ship quickly, validate impact, automate the boring parts → Strong grasp of growth metrics (LTV, CAC, MER, retention) and experiment design

Data engineer with 6+ years building reliable, scalable data pipelines that turn raw, messy data into systems businesses can actually trust and run on. I specialize in designing and automating ETL/ELT pipelines, modeling data warehouses, and optimizing data infrastructure so teams stop firefighting broken reports and start making decisions with confidence. My work spans the full data lifecycle — from ingestion and orchestration to transformation, testing, and documentation — using tools like [SQL, Python, Airflow, dbt, Snowflake/BigQuery — swap in her actual stack]. [Achievement/metric goes here — e.g. "Rebuilt a client's reporting pipeline, cutting manual processing time by 70% and eliminating recurring data errors."] I care about clean, well-documented systems over quick hacks — pipelines that the next person (or future me) can actually understand and maintain. I work closely with analytics and product teams to make sure the data answers the real business question, not just the technical one. If you need someone who can take a data mess and turn it into a dependable, automated system — let's talk about your project. I look forward to applying my problem-solving skills and technical expertise, enabling businesses to make data-driven decisions. I'm a person who is highly oriented, meticulous, and hard-working to ensure the user's problems are solved, scaling up products and improving all aspects of it. I also work closely with the product team to strategize and execute the development of data products. And Conduct mentorship sessions for software developer aspirants. Passionate about leveraging my skills and experience to contribute to dynamic organizations and drive meaningful results. Thank you for visiting my profile!! Open to all friendly suggestions and opportunities to connect professionally.

🚀 End-to-End Data Engineer | AWS | Redshift | Kafka | dbt | ETL Pipelines | Alerting & Compliance Hi! I’m a skilled Data Engineer who builds end-to-end data solutions that are scalable, secure, and production-ready. I specialize in designing ETL/ELT pipelines, real-time data streaming, data warehouse modeling, and automating alerts, monitoring, and compliance checks to ensure data reliability and governance. 🛠️ Key Skills & Tools • Cloud Platforms: AWS (Glue, Lambda, S3, CloudWatch), Azure • Data Warehousing: Amazon Redshift, Snowflake • Orchestration: Apache Airflow, AWS Step Functions • Data Modeling: dbt, SQL (Redshift, MySQL, PostgreSQL) • Streaming & Messaging: Apache Kafka, Kinesis • Scripting: Python, PySpark • Alerting & Monitoring: SNS, CloudWatch, custom SLA triggers • Data Quality & Compliance: Audit trails, rollback support, validation rules 📈 Projects & Accomplishments • Built scalable ingestion pipelines from MongoDB to Redshift using Kafka & Glue • Designed automated alerting systems for SLA monitoring & anomaly detection • Created dbt models with incremental logic, enabling fast and efficient data refresh • Delivered a full data migration framework with logging, retries, and compliance reporting • Optimized Redshift queries to reduce processing time by 40% 🎓 Education & Certification • B.Tech – Computer Science, NSIT • AWS Certified Data Analytics • dbt Fundamentals Certified Let’s work together to turn your raw data into reliable, actionable insights—with clean architecture and rock-solid pipelines. 🌐

I am a Data Engineer with over 15 years of software engineering experience, focusing on creating scalable data platforms. My expertise lies in designing efficient ELT pipelines, advanced data modeling, and implementing cloud analytics solutions with tools like Snowflake, dbt, and Azure/AWS. I excel at merging backend engineering with data engineering to develop reliable, production-ready data systems that drive informed decision-making. With a strong track record in leading projects and a commitment to optimizing data workflows, I am equipped to tackle complex challenges. If you need someone who understands the intricacies of data infrastructure and can deliver actionable insights, let’s connect and discuss how I can elevate your data strategy.

I’m a Data Engineer with 6+ years of experience designing and building scalable, reliable data solutions across Azure, AWS, and GCP. I specialize in creating robust ETL/ELT pipelines, optimizing data performance, and delivering clean, analytics-ready datasets that power dashboards, reporting, and business decision-making. I’ve worked end-to-end: from ingestion (files/APIs/enterprise systems) to transformation (Spark/SQL/dbt-style modeling), to storage/warehousing (Lakehouse/Data Warehouse), and finally enabling analytics with BI tools. My focus is always the same: data that is accurate, fast, well-modeled, and trustworthy. What I can help you with: Building and optimizing data pipelines (batch / incremental) using Azure Data Factory, Databricks/Spark, and cloud storage (ADLS / S3) Data modeling (staging → curated → marts), SQL tuning, and reliable transformations for reporting and analytics Data warehouse/lakehouse work with Snowflake and modern analytics workflows Data quality & validation (checks, reconciliations, monitoring, and reporting accuracy) Integrating data sources (including business systems like Salesforce/SAP-style sources) into a single source of truth Enabling BI layers for dashboards and KPIs (Power BI/Tableau/QuickSight), ensuring performance and consistency I’m easy to work with, very organized, and I like to communicate clearly: you’ll get a simple plan, milestones, and delivery that you can verify (queries, reconciliations, documentation, and handoff). If you tell me your data sources, your current setup, and the outcome you want (dashboards, migration, pipeline performance, automation, etc.), I can propose the cleanest approach with a clear scope and deliverables.

Senior Backend & Data Engineer with 6+ years of experience designing and building custom business systems, API integrations, ETL pipelines, data migrations, and reporting platforms. My background combines software engineering and data engineering. I've designed and implemented solutions ranging from backend APIs and automation tools to enterprise-scale migration frameworks, reporting systems, and data pipelines. In my current role, I've led multiple large-scale data migration initiatives involving complex entity mapping, relationship preservation, schema translation, automated validation, and end-to-end migration execution across enterprise engineering platforms. I have hands-on experience designing database schemas, building REST APIs, integrating third-party systems, automating business workflows, and creating reporting solutions used by management and operational teams. My work includes data extraction from APIs, transformation pipelines, automated reporting, dashboard integration, and system synchronization across multiple platforms. Core technologies include Python, PostgreSQL, SQLAlchemy, FastAPI, Docker, Airflow, dbt, Redis, Celery, REST APIs, Power BI, and CI/CD automation. Typical projects I help clients with: • API integrations and business process automation • ETL/ELT pipelines and reporting platforms • Data warehouse and reporting backend development • Database schema design and data modeling • Data migration and system modernization projects • Backend APIs and internal business applications • Workflow automation and operational reporting I focus on delivering maintainable solutions that solve real business problems, not just code that works for today. Whether the challenge is integrating multiple SaaS platforms, migrating legacy data, building reporting pipelines, or developing a backend system from scratch, I can help design and implement a reliable solution from architecture through delivery.

I am a Business Intelligence Engineer with 4 years of experience at a leading cloud computing company. I specialize in bridging the gap between raw, messy data and executive decision-making. Rather than just building basic dashboards, I architect scalable data pipelines, run rigorous statistical experiments, and leverage AI to automate complex analytics workflows. My goal is to transform your complex datasets into clear, actionable narratives that drive strategy. 🚀 My Core Strengths: End-to-End Pipeline Architecture: Designing, optimizing, and maintaining robust dbt workflows and telemetry pipelines to ensure data is clean, scalable, and delivered on time. LLM & AI Automation: Leveraging Large Language Models (LLMs) to automate heavy data processing, categorize unstructured datasets, and significantly reduce daily execution runtimes. Product Analytics & Experimentation: Applying rigorous statistical modeling and A/B testing to uncover actionable product insights and guide growth strategies. Business-Centric Delivery: Translating ambiguous stakeholder requirements into reliable data assets and dashboards that non-technical teams can trust. 🛠️ My Tech Stack & Tools: Programming & Scripting: Advanced SQL, Python, R Data Engineering & Transformation: dbt (Data Build Tool), ETL/ELT Pipelines AI & Advanced Analytics: LLM APIs, Statistical Modeling, A/B Testing Frameworks Cloud & BI Infrastructure: AWS (Redshift, S3, Athena), Tableau, Power BI, Snowflake If you are looking for an analytics expert who understands both the underlying data infrastructure and the high-level business strategy, let’s connect and discuss your project.