Are you looking for a results-driven BI & Analytics professional who can turn complex data into clear insights, automate workflows, and improve operational efficiency—without overshooting your budget? I’m a certified Alteryx Consultant and Data Analyst with strong expertise in Power BI, Tableau, SQL, and advanced analytics, helping organizations build scalable, real-time, and decision-ready data solutions. 🔧 Core Expertise ✨ Alteryx (Core, Advanced & Expert Certified) – Workflow automation, ETL pipelines, macros, data preparation, and server administration ✨ Power BI – Data modeling, DAX, KPI-driven dashboards, performance optimization ✨ Tableau – Interactive dashboards, data storytelling, and executive-level visualizations ✨ SQL – Data extraction, transformation, and analysis ✨ Databricks & Snowflake – Cloud data processing and analytics ✨ Python & R – Data analysis and visualization ✨ Advanced Excel & Power Query – Automation, reporting, and data cleansing 🔍 Why Choose Me? 💯 Strong analytical mindset with business-first thinking 📜 Multiple industry-recognized certifications ⚡ Focus on automation, accuracy, and scalability 📊 Expertise in operational analytics, supply chain, and process improvement 🕒 Clear communication and reliable deliveryBFSI Analytics – MIS reporting, risk & profitability insights 🏦 Finance Domain Expertise – Banking KPIs, compliance & cost optimization 📊 BI & Analytics Services I Offer ✔ End-to-end Alteryx workflows, ETL automation & server support ✔ Power BI & Tableau dashboards aligned with business KPIs ✔ Data modeling, cleaning, and advanced analysis ✔ SQL query optimization and reporting ✔ One-on-one training & mentoring for Alteryx, Power BI, and Tableau ✔ Supply chain, inventory, and operational performance analytics 🏭 Domain Knowledge 📦 Supply Chain Management 📉 Inventory Control & Optimization ⏱ Time & Motion Studies 📐 Six Sigma Methodology 📈 Process Improvement & Operational Efficiency 💰 Finance & Financial Analytics 🏬 Retail Analytics & Sales Performance 🛒 E-commerce Analytics (Customer behavior, funnel analysis, revenue optimization) 🎓 Certifications ✔ Alteryx Core, Advanced & Expert Certified ✔ Alteryx Server Administrator Certified ✔ Microsoft Certified: Power BI Data Analyst Associate (PL-300) ✔ Tableau Desktop Certified 📬 Let’s Work Together! If you need a trusted BI consultant, Alteryx trainer, or dashboard expert, let’s discuss how I can help you turn data into measurable business value. Highlighted Services: Alteryx Consultant | Alteryx Trainer | Power BI Expert | Tableau Developer | SQL Analyst | ETL Automation | BI Dashboards | Data Analytics | Supply Chain Analytics

I am a seasoned Data Engineer and Big Data Specialist with over 7 years of experience in building and optimizing scalable data pipelines, crafting real-time analytics solutions, and leveraging cloud technologies to deliver business value. My expertise spans across Big Data Ecosystems, Cloud Platforms, and Data Warehousing, ensuring end-to-end solutions for data-driven decision-making. 𝗗𝗼𝗺𝗮𝗶𝗻 𝗘𝘅𝗽𝗲𝗿𝘁𝗶𝘀𝗲 : 𝗣𝗵𝗮𝗿𝗺𝗮𝗰𝗲𝘂𝘁𝗶𝗰𝗮𝗹 𝗜𝗻𝗱𝘂𝘀𝘁𝗿𝘆 – Extensive experience in clinical data management, working with IQVIA, Sanofi, and optimizing study data pipelines for pharmaceutical research and trials. 𝗙𝗶𝗻𝗮𝗻𝗰𝗶𝗮𝗹 𝗦𝗲𝗰𝘁𝗼𝗿 – Expertise in the insurance domain, handling claims processing, policy data management, and ensuring compliance with industry regulations. 𝗥𝗲𝗰𝗿𝘂𝗶𝘁𝗺𝗲𝗻𝘁 𝗜𝗻𝗱𝘂𝘀𝘁𝗿𝘆 – Worked with job advertisement data, company insights, geographical talent search, and salary analytics to enhance recruitment strategies. 𝐊𝐞𝐲 𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬: Extensive experience in Big Data Analytics, specializing in Apache Spark, Kafka, NIFI, and Databricks, with a strong focus on Delta Lake, Streaming, and Batch Processing. Proficient in building robust ETL/ELT pipelines and designing OLAP data models using technologies Redshift, Snowflake, and BigQuery with dbt transformation. Hands-on expertise in AWS (Glue, S3, EMR, Lambda, Step Functions) and Azure (Databricks, ADF, CosmosDB) to build scalable, cloud-native architectures. Experienced in Docker containerization and deploying distributed applications across various environments. Adept at orchestrating data pipelines with Apache Airflow, Jenkins, and automated CI/CD workflows to streamline production deployments. Skilled in programming with Python and Pandas, with exposure to Scala for efficient data transformation and processing. 𝐋𝐞𝐚𝐝𝐞𝐫𝐬𝐡𝐢𝐩 & 𝐂𝐨𝐥𝐥𝐚𝐛𝐨𝐫𝐚𝐭𝐢𝐨𝐧: Proven track record in mentoring and leading technical teams, ensuring high-quality deliverables and fostering innovation. Experienced in collaborating with cross-functional teams, translating complex requirements into scalable solutions, and ensuring alignment with business goals. Passionate about adopting Agile (Scrum) and DevOps methodologies to enhance development efficiency and team collaboration. 𝐖𝐡𝐲 𝐂𝐨𝐧𝐧𝐞𝐜𝐭 𝐖𝐢𝐭𝐡 𝐌𝐞? I thrive on solving complex data challenges, driving impactful insights. Whether it’s optimizing data architectures, building real-time analytics solutions, or mentoring teams, Let’s connect and discuss how we can shape the future of data together!

I build data platforms and AI systems that run in production — pipelines, RAG and agent applications, evaluation and monitoring, and the infrastructure underneath. 9+ years shipping, on all three major clouds. Multi-cloud, for real: I've built and operated production platforms on AWS, GCP, and Azure — not one cloud plus certifications in the other two. That includes BigQuery at 4 PB/day, Kubernetes (EKS/GKE/AKS) for GPU workloads, and Terraform-managed infrastructure across all three. B2B SaaS and regulated industries: I currently work on an enterprise AI observability platform, and most of my delivery experience is with banking, insurance, and telecom clients — environments where audit trails, PII handling, data residency, and access controls aren't optional. Familiar with EU AI Act, ISO 42001, NIST AI RMF, AIUC, and LGPD/GDPR requirements as they land on engineering teams. Recent work: - Multi-cluster GPU job orchestration on Azure Kubernetes (AKS Fleet Manager, MultiKueue) for an autonomous trucking company - Airflow + MLflow platform serving multiple ML teams — DAG design, version migrations, CI/CD, cost control - Data platform processing 4 PB/day on BigQuery — 50% cloud cost reduction, 85% data quality improvement, team of 25 - Enterprise LLM evaluation and observability: tracing, guardrails, LLM-as-judge, regression testing - ML research on passive sonar with the Brazilian Navy — published internationally, prototype deployed on a submarine Data engineering - Batch and streaming pipelines, warehouse and lakehouse design, dbt modeling, data quality and observability, migrations, cost optimization. PII handling, lineage, and audit-ready pipelines for regulated workloads. Airflow, Spark, Kafka, dbt, BigQuery, Databricks, Snowflake and Redshift. AI engineering - RAG systems, LLM agents, evaluation pipelines, guardrails, tracing and monitoring, model serving. LangChain/LangGraph, vector databases, OpenAI/Anthropic/Bedrock/Vertex AI. ML infrastructure - Training and inference on Kubernetes, GPU scheduling, MLflow, feature pipelines, CI/CD for ML, Terraform. EKS, GKE, AKS. I'm useful whether you need a pipeline built from scratch, a broken one diagnosed, or a proof-of-concept turned into something that won't fall over at scale. Most engagements start small and run long — 14 contracts, 2,500+ hours, $200K+ earned on Upwork. Stack: Python, SQL, PyTorch, FastAPI, Airflow, dbt, Spark, BigQuery, Databricks, Snowflake, MLflow, Kubernetes, Docker, Terraform, AWS, GCP, Azure. Tell me what you're building or what's broken. If I'm not the right fit, I'll say so.

• Senior Data Engineer and Data Architect with 18+ years of experience delivering enterprise data solutions across insurance, healthcare, pharmaceuticals, and financial services. • Specialized in Snowflake Data Platform, dbt, Big Data, Python, Apache Spark, Data Pipelines, Data Warehousing, and Cloud Data Integration. • Proven expertise in designing end-to-end data architecture, building ETL/ELT pipelines, and developing scalable, high-performance data platforms for analytics, reporting, and business decision-making. • Strong background in data migration, historical data validation, data reconciliation, data quality improvement, and performance optimization across complex enterprise environments. • Hands-on experience helping organizations modernize data ecosystems using Snowflake, AWS, Python, Spark, and modern data engineering best practices. • Completed a Post Graduate Diploma in Data Science and Business Analytics from UT Austin, with knowledge in Machine Learning, advanced analytics, and data-driven problem solving. • Ideal for clients seeking support with Snowflake implementation, Big Data engineering, Python development, Spark processing, Data Pipeline design, Cloud Migration, and Modern Data Architecture.

As a seasoned Data Engineer with 8 years of rich experience in crafting scalable data solutions, I excel in transforming complex data challenges into streamlined, efficient systems that drive business growth. With a Master's degree in Data Engineering, I bring deep expertise in data architecture, pipeline construction, and automation, underpinned by a robust proficiency in programming languages like Python and SQL, alongside mastery in big data frameworks and cloud platforms (AWS, Azure, GCP). My career highlights include leading strategic data migrations, developing cutting-edge analytics systems, and pioneering advanced ETL processes that significantly enhance operational efficiency and decision-making capabilities. At the heart of my approach is a commitment to leveraging technology to solve complex problems, and collaborating closely with cross-functional teams to foster a data-centric culture within organizations. I'm here to offer my extensive skills in data processing and analytics, database management, data visualization, and DevOps practices to empower your business with data-driven insights and solutions. Let's harness the power of your data to unlock new opportunities and propel your business forward.

Aditya is a Senior Data Engineer with over 5 years of experience in designing and implementing scalable, cloud-native data platforms, primarily in the financial services sector. He specializes in building AWS-native pipelines for multi-asset datasets, real-time Kafka streaming systems, and robust Medallion Architectures that support multiple business domains. Aditya has deep expertise in tools such as Snowflake, dbt, Dagster, and PySpark, which he leverages to significantly improve data processing efficiency. Throughout his career, he has consistently enhanced SLA compliance, reduced pipeline runtimes, and eliminated substantial manual engineering effort on an annual basis. If you’re looking for a data engineering professional who can optimize your data pipelines and deliver impactful, scalable solutions, Aditya is open to connecting and discussing how he can contribute to your project.

𝗕𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗜𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲 𝗖𝗼𝗻𝘀𝘂𝗹𝘁𝗮𝗻𝘁 | 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 | 𝗗𝗮𝘁𝗮 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 | 𝗘𝗧𝗟 | 𝗗𝗮𝘁𝗮 𝗪𝗮𝗿𝗲𝗵𝗼𝘂𝘀𝗶𝗻𝗴 I help businesses turn raw data into accurate reporting, interactive dashboards, automated workflows, and actionable insights. With 8+ years of experience in Business Intelligence, Data Analytics and Data Engineering, I build scalable data solutions that support better business decisions across eCommerce, SaaS, Finance, Marketing, Healthcare and Real Estate. 𝗕𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗜𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲 & 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀: • Executive KPI Dashboards • Business Intelligence Solutions • Marketing, Sales & Financial Analytics • Customer, Product & Inventory Analytics • Forecasting & Predictive Analytics • Marketing Attribution Modeling • Customer Lifetime Value (LTV) & CAC Analysis • Financial Reporting & Modeling • Data Strategy & Consulting 𝗗𝗮𝘀𝗵𝗯𝗼𝗮𝗿𝗱𝘀 & 𝗩𝗶𝘀𝘂𝗮𝗹𝗶𝘇𝗮𝘁𝗶𝗼𝗻: • Power BI • Tableau • Looker Studio • Sigma Computing • Klipfolio • Microsoft Excel • Google Sheets I create clean, interactive dashboards that provide real-time visibility into business performance, marketing ROI, revenue, profitability, customer behavior, and operational KPIs. 𝗗𝗮𝘁𝗮 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 & 𝗘𝗧𝗟: • ETL / ELT Pipeline Development • Data Integration & Automation • Data Cleaning & Transformation • Data Validation & Quality Monitoring • Data Modeling & Pipeline Optimization **𝗧𝗼𝗼𝗹𝘀:** Python, SQL, dbt, Apache Airflow, Fivetran, Stitch, Airbyte, Zapier, Make 𝗗𝗮𝘁𝗮 𝗪𝗮𝗿𝗲𝗵𝗼𝘂𝘀𝗶𝗻𝗴 & 𝗗𝗮𝘁𝗮𝗯𝗮𝘀𝗲𝘀: • Snowflake • Google BigQuery • Amazon Redshift • Azure Synapse • PostgreSQL • MySQL • SQL Server • MongoDB I design scalable cloud data warehouses and optimize databases for fast, reliable analytics. 𝗲𝗖𝗼𝗺𝗺𝗲𝗿𝗰𝗲 & 𝗠𝗮𝗿𝗸𝗲𝘁𝗶𝗻𝗴 𝗜𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗶𝗼𝗻𝘀: • Shopify Plus & Shopify API • Amazon Seller Central & SP-API • Meta Business Suite & Facebook Ads API • Google Ads API • Google Analytics • Klaviyo API • Stripe • QuickBooks • HubSpot • Salesforce • Zoho CRM • Pipedrive 𝗣𝗿𝗼𝗴𝗿𝗮𝗺𝗺𝗶𝗻𝗴 & 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀: • Python (Pandas, NumPy, Scikit-learn) • SQL • R • Excel VBA • Power Query • Machine Learning • Time Series Forecasting • Statistical Analysis • Web Scraping 𝗪𝗵𝘆 𝗪𝗼𝗿𝗸 𝗪𝗶𝘁𝗵 𝗠𝗲: - 8+ Years of Professional Experience - Business-First Approach - Reliable ETL & Data Pipelines - Executive-Level Dashboards - Clean, Well-Documented Solutions - Fast Communication & On-Time Delivery - Long-Term Support 📈 𝗟𝗲𝘁’𝘀 𝘁𝘂𝗿𝗻 𝘆𝗼𝘂𝗿 𝗱𝗮𝘁𝗮 𝗶𝗻𝘁𝗼 𝘀𝘁𝗿𝗮𝘁𝗲𝗴𝗶𝗰 𝗴𝗿𝗼𝘄𝘁𝗵 𝗮𝗻𝗱 𝗺𝗲𝗮𝘀𝘂𝗿𝗮𝗯𝗹𝗲 𝗿𝗲𝘀𝘂𝗹𝘁𝘀!

I am a data engineer with over six years of experience building data infrastructure that teams trust and use every day. Based in Australia, I have delivered end-to-end data solutions across finance, retail, healthcare, and SaaS, working with modern cloud platforms, distributed processing frameworks, and the full data engineering toolchain. On the pipeline and orchestration side, I design and build batch and streaming ETL and ELT workflows using Apache Airflow, Prefect, and AWS Glue. I have built real-time ingestion pipelines with Apache Kafka and AWS Kinesis, and I use Apache Spark and PySpark for large-scale distributed data processing. For transformation and modelling, dbt is my primary tool and I have structured dbt projects with tests, documentation, and CI integration across multiple production environments. My cloud experience covers all three major platforms. On AWS I regularly work with S3, Redshift, Glue, Lambda, Athena, Step Functions, and RDS. On Google Cloud I have built solutions using BigQuery, Dataflow, Cloud Composer, Pub/Sub, and Cloud Storage. On Azure I have worked with Azure Data Factory, Azure Synapse Analytics, Azure Databricks, and ADLS Gen2. I am comfortable architecting solutions from scratch on any of these platforms and I know how to keep costs reasonable as data volumes grow. For data warehousing and lake architecture, I design schemas in Snowflake and BigQuery, implement medallion and lakehouse architecture patterns, and work with Delta Lake and Apache Iceberg for open table formats. I understand dimensional modelling well and I apply star schema and snowflake schema design based on what actually fits the use case rather than defaulting to one approach for everything. My programming background is primarily Python and SQL. In Python I use Pandas, PySpark, SQLAlchemy, and FastAPI depending on what the project needs. I version control everything through Git and I write infrastructure as code using Terraform. For containerisation and deployment I work with Docker and I have set up data pipelines running on Kubernetes clusters. I have also integrated data workflows into CI/CD pipelines using GitHub Actions and GitLab CI. On the analytics and reporting side, I have connected data layers to Power BI, Tableau, Looker, and Metabase. I work closely with analysts and data science teams to ensure they have clean, well-documented datasets and feature tables to work from. I take data quality seriously and I implement Great Expectations and dbt tests as standard practice on production pipelines rather than as an afterthought. I communicate clearly, deliver on time, and raise problems early. If you need a data engineer who knows the stack deeply and can get a project moving without hand-holding, I am available for both fixed-scope projects and ongoing engagements. Python | SQL | Data Engineering | ETL | ELT | Data Pipelines | Pipeline Development | Data Processing | Batch Processing | Stream Processing | Real-Time Data | Apache Airflow | Prefect | AWS Glue | Apache Kafka | AWS Kinesis | Apache Spark | PySpark | dbt | Data Transformation | Data Modeling | Data Warehousing | Data Lake | Data Lakehouse | Medallion Architecture | Delta Lake | Apache Iceberg | Dimensional Modeling | Star Schema | Snowflake Schema | Data Architecture | Big Data | Distributed Systems | Data Infrastructure | Data Integration | Data Migration | Data Ingestion | Data Validation | Data Quality | Great Expectations | Data Governance | Data Catalog | Feature Engineering | Feature Store | Analytics Engineering | Business Intelligence | Data Analytics | Reporting | Dashboarding | Power BI | Tableau | Looker | Metabase | Snowflake | BigQuery | Amazon Redshift | Amazon S3 | AWS | AWS Lambda | AWS Athena | AWS Step Functions | AWS RDS | Google Cloud Platform | GCP | BigQuery | Dataflow | Cloud Composer | Pub/Sub | Cloud Storage | Microsoft Azure | Azure Data Factory | Azure Synapse | Azure Databricks | ADLS Gen2 | Databricks | SQLAlchemy | Pandas | FastAPI | Docker | Kubernetes | Terraform | Infrastructure as Code | CI/CD | GitHub Actions | GitLab CI | Version Control | Git | Scalable Systems | High Performance Systems | Cost Optimization | Monitoring | Logging | Observability | SaaS | Fintech | Retail | Healthcare | E-commerce | Logistics | Supply Chain | Real Estate | EdTech | Media | Startups | Enterprise Applications | B2B Platforms | B2C Platforms

Most Power BI problems aren't really Power BI problems. A report is slow because the ETL feeding it is a mess. Numbers are wrong because two source systems never agreed in the first place and nobody checked. I work on the layer underneath. Get that right and the dashboard part becomes the easy bit. I've spent 5+ years building, fixing, and scaling end-to-end reporting systems across Power BI, Microsoft Fabric, and open source stacks when those are the better fit. Things I can take off your plate: setting up data infrastructure, building Lakehouse and Warehouse pipelines, migrating Power Query-heavy reports to Fabric, optimizing slow Power BI models, connecting APIs and databases, implementing DAX and RLS, and building executive or operational dashboards. Some numbers from the last 5 years, because numbers travel better than adjectives: - Cut one client's data refresh time by 90%. All I really did was move their Power Query ETL into Microsoft Fabric. The reports stayed the same. Everything underneath them changed. - Took a reporting cycle from 7 days down to 1. The fix had nothing to do with the report itself. Two messy sources needed manual reconciliation every single week, so I automated that validation to run before reporting even starts. - New client report setup used to eat a full day. It's under an hour now, because the entire Fabric + Power BI stack is templated. - CAPEX budget utilization went from 50% to 80% after I built a single source-of-truth dashboard. When everyone argues from the same numbers, money stops sitting idle. - The one I'm proudest of: an enterprise client came back and asked us to replace ALL their legacy reporting with the Fabric warehouse I'd built. Right now I'm consulting on a multi-tenant Fabric platform. One reporting suite, many clients, each securely seeing only their own data through RLS and tenant isolation. If your reports are slow, wrong, or late, message me what's happening and I'll tell you where I'd look first. ---------------------------------------- 🎯 Core Expertise: ✅ Microsoft Fabric Platforms: End-to-end implementations covering lakehouses, warehouses, pipelines, and semantic models on Medallion architecture (bronze/silver/gold). I've consolidated multi-region data into a single EDW and handled schema evolution without the usual breakage. ✅ Power BI Development & Optimization: Star-schema models, DAX that actually performs, and reports executives open more than once. My specialty is fixing slow reports at the root, which usually means dragging heavy Power Query logic upstream where it belongs. ✅ ETL & Data Pipelines: Parametrized, templated pipelines in Fabric and Airflow, built so onboarding a new client or project is configuration, not rebuilding. That's where the 1-day-to-under-an-hour number comes from. ✅ Data Governance & Security: Row-level security done properly (board, executive, and manager tiers on a single model), workspace security, SharePoint access governance for external partners, and multi-tenant isolation so each client sees only their own data. ✅ Data Quality & Validation: Governance dashboards that check and reconcile fragmented sources BEFORE reporting starts, not after someone spots a wrong figure in a board pack. This is what cut that client's cycle from 7 days to 1. ✅ CI/CD & Source Control for BI: Version-controlled reports and Fabric artifacts with automated deployment pipelines. Production releases should be boring and reversible. Mine are. ✅ Scalable Data Products: Reusable source templates, standard ETL, standard reporting. Plugging in a new project of the same scale is basically plug-and-play. ✅ API Integration & Near-Realtime Reporting: Custom ingestion from REST APIs (Azure DevOps, construction platforms like Aconex and MiTek) feeding near-realtime Power BI dashboards. ⚙️ Tech Stack: ⚡ Platform & Lakehouse: Microsoft Fabric | Lakehouse | Data Warehouse | Medallion Architecture | Delta Tables ⚡ BI & Reporting: Power BI | DAX | Power BI Service | Power BI Apps | RLS | Paginated & Audience-Specific Reporting ⚡ Transformation & Processing: Spark | PySpark | SQL | Python | Data Modeling | Star Schema ⚡ Orchestration & Pipelines: Fabric Data Pipelines | Apache Airflow | Azure Pipelines | Docker ⚡ Governance & Security: Row-Level Security | Workspace Governance | SharePoint Access Control | Multi-Tenant Isolation | CI/CD | Git Source Control ⚡ Databases: PostgreSQL | MySQL | ClickHouse | DuckDB ⚡ Integration: REST APIs | Azure DevOps API | SAP Data Sources | Excel Automation | SharePoint | Power Automate

As an experienced data engineer with over five years of experience, I specialize in designing and implementing scalable data solutions. I am proficient in ETL development, data warehousing, and data modeling. My skills include optimizing data pipelines, ensuring data quality, and supporting business decision-making with efficient data engineering practices. ​I have developed and maintained scalable ETL pipelines using Airbyte and dbt to support data warehousing and analytics workflows. I have also automated data ingestion from various sources into AWS-based data lakes and warehouses, ensuring high availability and accuracy. I am experienced in orchestrating workflows using Apache Airflow to enable timely and reliable data transformations and reporting. I can write optimized SQL and Python scripts for complex data modeling, transformation, and validation tasks. I have experience collaborating with data analysts, data scientists, and business stakeholders to define requirements and deliver high-impact data solutions. ​My expertise includes building high-performance ETL pipelines for machine learning workflows using Python, DuckDB, and Apache Airflow. I have also developed machine learning-ready data pipelines and normalized ERP datasets. Furthermore, I have designed scalable ETL workflows and dashboards using Alibaba Cloud. I have a strong background in improving operational efficiency through data solutions and handling ad hoc data requests. ​I am certified as a Databricks Data Engineer Associate. My technical skills include Python and Java programming; Big Data and Cloud technologies such as Apache Spark, Google BigQuery, and AWS; ETL tools like Talend Open Studio; and databases including MongoDB and SQL. I am also proficient in orchestration tools like Apache Airflow and Prefect.