Hi, I’m Anita, managing this Upwork account. Our projects are led by Pankaj, a Principal Data Engineer with 19+ years of experience, supported by a team of skilled data engineers, cloud specialists, and analytics professionals. Together, we deliver end-to-end data engineering and analytics solutions — from design to deployment and training. Most of our experience lies in the banking, finance, and e-commerce domains, where we have built and optimized large-scale data platforms for risk management, fraud detection, customer analytics, and regulatory reporting — ensuring performance, scalability, and reliability. 🔹 Key Expertise Big Data & Spark: 10+ years of experience with PySpark, Spark SQL, Spark Scala, and Structured Streaming for large-scale and streaming data pipelines. Programming Languages: Proficient in Python and Scala for ETL, automation, and distributed data processing. Cloud & Platforms: Expertise across AWS (S3, Glue, EMR, Redshift, Kinesis), Azure (Data Factory, Synapse, Databricks), and GCP (BigQuery, Dataflow, Pub/Sub). Modern Data Stack: Hands-on with dbt, Snowflake, and Apache Airflow for ELT, orchestration, and data modeling. Domain Knowledge: Strong experience in banking data engineering, plus e-commerce analytics, recommendation engines, and customer insights. Team Capabilities: Our team can handle complete data projects — including architecture setup, pipeline development, performance tuning, and dashboard delivery. Training & Mentorship: We also provide corporate and individual training in Python, PySpark, and modern data engineering, using real-world projects like: “Designing a Kafka → PySpark → Snowflake → dbt → Airflow data pipeline.” We combine deep technical expertise, agile delivery, and strong business understanding to deliver scalable, cloud-native, and future-ready data platforms tailored to your goals. Let’s connect to discuss how our team can help you design, build, and deliver robust, modern data engineering solutions for your business. — Anita (Account Manager) & Pankaj (Principal Data Engineer & Delivery Lead)

I help growing organizations transform fragmented, unreliable data into trusted analytics systems that support confident decision-making. With 7+ years of experience across data engineering, analytics engineering, architecture, and team leadership, I design and implement scalable data solutions from source systems through reporting and business insight. My work focuses on creating sustainable analytics foundations that teams can trust and build upon as they grow. Recent highlights include: • Built and led a centralized analytics function from the ground up, designing a modern data platform using dbt, ELT pipelines, and semantic modeling that increased data efficiency and utilization by 80%. • Architected and deployed a national-scale analytics solution on Azure, modeling and serving over 1.6 billion data points to support market segmentation, strategic planning, and executive decision-making. • Partnered directly with executive leadership to translate complex business challenges into scalable data systems, dashboards, and analytics frameworks that informed operational and go-to-market strategy. Beyond implementation, I specialize in bridging the gap between technical and non-technical stakeholders. I create clear architecture documentation, technical specifications, and stakeholder-facing materials that make complex systems understandable, maintainable, and actionable. Whether you need to modernize your analytics stack, design a data platform from the ground up, improve reporting trust, or establish scalable data practices, I can help you build a foundation that delivers long-term value.

I’m a Data Engineer based in Buenos Aires, 32 years old, with a strong interest in sports and running. I bring that same discipline, resilience, and consistency into my work—building data systems that are reliable, scalable, and built to last. I’m methodical in how I approach problems, but also easy to work with and comfortable collaborating across teams. I am a Data Engineer with over 5 years of experience designing, building, and maintaining scalable data systems for U.S.-based companies. I specialize in developing reliable data pipelines, well-modeled warehouses, and production-ready analytics layers that support both business intelligence and advanced use cases. My core technical skill set includes SQL (Snowflake, BigQuery) and Python, with hands-on experience across AWS and GCP. I’ve built and optimized end-to-end data pipelines—from ingestion and transformation to modeling and serving—using modern data stack best practices. I regularly work with dbt to implement robust data models, enforce data quality, and ensure reproducibility and transparency across environments. I have strong experience designing data warehouses that scale, optimizing query performance, managing incremental loads, and handling large datasets efficiently. I’m comfortable owning data systems in production, collaborating closely with stakeholders, and translating business requirements into durable, well-engineered solutions. My background working across the full data lifecycle allows me to deliver systems that are not only technically sound, but also practical, maintainable, and aligned with real business needs. I’m best suited for teams looking for a hands-on data engineer who can move quickly, stay focused under pressure, and take ownership while being a reliable and collaborative teammate.

I’m confident Big Data Engineer with over 5 years of experience in designing, implementing, and optimizing large-scale data processing systems. I have expertise in building robust ETL pipelines, developing and maintaining comprehensive data warehouses, and executing seamless data migration and replication tasks. With a solid foundation in data engineering principles, I have successfully transformed raw data into actionable insights, enabling data-driven decision-making across various industries. My experience includes managing large datasets, ensuring data integrity, and enhancing data processes to meet business needs. Adept at working with diverse teams and communicating technical concepts to non-technical stakeholders, I thrive in dynamic environments and am passionate about harnessing the power of data to drive innovation.

🚀 4+ 𝐘𝐞𝐚𝐫𝐬 𝐨𝐟 𝐄𝐱𝐩𝐞𝐫𝐢𝐞𝐧𝐜𝐞 𝐢𝐧 𝐂𝐥𝐨𝐮𝐝 𝐃𝐚𝐭𝐚 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐢𝐧𝐠 | ☁️ 𝐀𝐖𝐒 𝐂𝐞𝐫𝐭𝐢𝐟𝐢𝐞𝐝 + 𝐌𝐮𝐥𝐭𝐢-𝐂𝐥𝐨𝐮𝐝 𝐄𝐱𝐩𝐞𝐫𝐭 | 🏆 𝐃𝐞𝐥𝐢𝐯𝐞𝐫𝐞𝐝 𝟏𝟐𝟎+ 𝐃𝐚𝐭𝐚 𝐏𝐫𝐨𝐣𝐞𝐜𝐭𝐬 𝐖𝐨𝐫𝐥𝐝𝐰𝐢𝐝𝐞 | 📊 𝐒𝐩𝐞𝐜𝐢𝐚𝐥𝐢𝐬𝐭 𝐢𝐧 𝐄𝐓𝐋, 𝐖𝐚𝐫𝐞𝐡𝐨𝐮𝐬𝐢𝐧𝐠 & 𝐀𝐧𝐚𝐥𝐲𝐭𝐢𝐜𝐬 𝐀𝐮𝐭𝐨𝐦𝐚𝐭𝐢𝐨𝐧 I’m an 𝐀𝐖𝐒 𝐂𝐞𝐫𝐭𝐢𝐟𝐢𝐞𝐝 𝐃𝐚𝐭𝐚 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫 𝐚𝐧𝐝 𝐂𝐥𝐨𝐮𝐝 𝐒𝐨𝐥𝐮𝐭𝐢𝐨𝐧 𝐀𝐫𝐜𝐡𝐢𝐭𝐞𝐜𝐭 with expertise across AWS, Azure, GCP, Apache Airflow, and Python. I design and build scalable, automated data infrastructures that transform raw data into actionable insights. I specialize in creating 𝐄𝐓𝐋 𝐩𝐢𝐩𝐞𝐥𝐢𝐧𝐞𝐬, 𝐝𝐚𝐭𝐚 𝐰𝐚𝐫𝐞𝐡𝐨𝐮𝐬𝐞𝐬 𝐚𝐧𝐝 𝐚𝐧𝐚𝐥𝐲𝐭𝐢𝐜𝐬 𝐬𝐨𝐥𝐮𝐭𝐢𝐨𝐧𝐬 that are secure, high-performing, and cost-efficient. My focus is helping teams make faster, data-driven decisions with reliable, cloud-native systems. 𝐂𝐨𝐫𝐞 𝐄𝐱𝐩𝐞𝐫𝐭𝐢𝐬𝐞: ⚙️ Data pipeline orchestration: Airflow, AWS Glue, Azure Data Factory 📦 Data warehousing & lakes: Redshift, Snowflake, BigQuery 🧱 Database design & modeling: SQL, NoSQL, PostgreSQL, MongoDB 📊 BI integration: Power BI, Tableau, QuickSight, Looker Studio ☁️ Cloud architecture: AWS, Azure, GCP 💻 Programming Languages: Python, SQL, Java, C#, Shell Scripting With 𝟑+ of experience delivering global data engineering projects, I bring a mix of technical depth and business understanding, ensuring your data platforms are optimised for performance, scalability and long-term growth. 📩 Let’s connect to build or optimise your cloud data systems. 🔑 𝐊𝐞𝐲𝐰𝐨𝐫𝐝𝐬 #DataEngineer #AWS #AWSDataEngineer #Azure #AzureDataEngineer #GCPDataEngineer #ETLPipelines #DataWarehousing #ApacheAirflow #PythonDeveloper #Snowflake #Redshift #BigQuery #PowerBI #Tableau #DataArchitecture #CloudEngineer #DataAnalytics #DataPipelineAutomation #Database #SQL #python #LockerStudio

Data & AI Engineer | BI Dashboards, ETL Pipelines & Cloud Data Solutions I help businesses transform raw, scattered data into reliable data pipelines, clean data models, and actionable dashboards that support faster, smarter decision-making. With 3+ years of hands-on experience across enterprise and industrial environments, including projects with Continental, Mercedes-Benz, and other business clients, I specialize in building end-to-end data solutions that are scalable, maintainable, and business-focused. My core expertise includes: * BI & Dashboarding: Power BI, Tableau, and Excel dashboards, interactive reporting, data model optimization, DAX performance tuning, and star/snowflake schema design for scalable analytics. * ETL/ELT & Data Engineering: Designing, building, and orchestrating pipelines using Python, SQL, Airflow, dbt, and Apache Spark. I integrate data from SAP, Redshift, SharePoint, APIs, SQL databases, and other sources into clean, business-ready data layers. * Cloud Data Platforms: Azure Databricks, Azure Data Factory, Log Analytics, Azure DevOps, AWS S3, Redshift, EC2, and DynamoDB for scalable storage, processing, deployment, and monitoring. * Monitoring & Observability: Grafana, ELK Stack, Elasticsearch, Logstash, Kibana, and KQL-based dashboards for pipeline health, system performance, error tracking, and operational visibility. * AI & Machine Learning: Applying AI/ML techniques to real-world data and signal-processing use cases, including model evaluation, deployment, and practical business implementation. * Automation: Python and Excel VBA automation for recurring reports, manual workflow reduction, improved accuracy, and faster delivery. I am comfortable owning a project from start to finish: understanding business requirements, designing the data architecture, building pipelines, modeling data, and delivering polished dashboards that stakeholders can actually use. Whether you need a one-time dashboard, an ETL pipeline rebuild, cloud data migration, data automation, or ongoing BI support, I bring a structured, detail-oriented approach with clear communication throughout the project. Let’s discuss your data challenges and turn them into reliable, decision-ready insights.

I'm a Data Engineer with over 4 years of experience designing, building, and optimizing data pipelines and analytics solutions. I specialize in transforming raw data into reliable, business-ready datasets using modern data stack technologies. My expertise includes: Snowflake data warehousing dbt (data transformations, testing, and documentation) Advanced SQL development and query optimization ETL/ELT pipeline development Data modeling and data quality Performance optimization Advertising and marketing data integration (Meta, LinkedIn, X/Twitter, Google Ads, SA360, and more) In my current role, I work with large-scale advertising datasets for a global automotive client, building and maintaining data models that support reporting and business intelligence. I have experience migrating legacy SQL transformations into dbt projects while improving maintainability, testing, and documentation. I enjoy solving complex data problems, improving pipeline performance, and delivering clean, scalable solutions that businesses can trust. If you're looking for someone who communicates clearly, writes high-quality SQL, and delivers reliable data solutions, I'd be happy to help with your project.

🚀Hi! I help startups go from demo → market-ready AI product - fast! I plug into your team and ship production ready features that customers love. 🏆 Top 1% (Expert-vetted) | 8+ years| 100% JSS | 35+ Delighted Clients | 18+ E2E Deployments 🎯 "𝘒𝘶𝘯𝘫𝘢𝘯 𝘪𝘴 𝘢𝘯 𝘢𝘣𝘴𝘰𝘭𝘶𝘵𝘦 𝘨𝘦𝘮! 𝘚𝘩𝘦 𝘸𝘦𝘯𝘵 𝘢𝘣𝘰𝘷𝘦 𝘢𝘯𝘥 𝘣𝘦𝘺𝘰𝘯𝘥. 𝘛𝘦𝘢𝘮𝘪𝘯𝘨 𝘶𝘱 𝘸𝘪𝘵𝘩 𝘩𝘦𝘳, 𝘸𝘦 𝘣𝘶𝘪𝘭𝘵 𝘳𝘰𝘣𝘶𝘴𝘵 𝘥𝘢𝘵𝘢 𝘱𝘪𝘱𝘦𝘭𝘪𝘯𝘦𝘴, 𝘰𝘱𝘵𝘪𝘮𝘪𝘻𝘦𝘥 𝘓𝘓𝘔𝘖𝘱𝘴, 𝘵𝘳𝘢𝘯𝘴𝘪𝘵𝘪𝘰𝘯𝘦𝘥 𝘧𝘳𝘰𝘮 𝘧𝘢𝘯𝘤𝘺 𝘥𝘦𝘮𝘰𝘴 𝘵𝘰 𝘢 𝘴𝘤𝘢𝘭𝘢𝘣𝘭𝘦 𝘈𝘐 𝘱𝘳𝘰𝘥𝘶𝘤𝘵 𝘵𝘩𝘢𝘵 1000𝘴 𝘰𝘧 𝘤𝘶𝘴𝘵𝘰𝘮𝘦𝘳𝘴 𝘭𝘰𝘷𝘦!" - CTO, Healthcare startup, SF (1 of many 5-star reviews) Over 7+ years on Upwork, I've helped startups and enterprises across sectors - Marketing, Healthcare, Law, Education, and Agribusiness - translate AI into measurable business ROI by building the right data and ML foundations. Whether collaborating with 1000+ employee companies or lean 2-person startups, I adapt to the team’s needs: reporting directly to CEOs or CTOs as an AI/Data Lead, or integrating as a hands-on contributor alongside in-house developers. I bring a “brain-for-hire” + hands-on mindset - tackling strategic and technical challenges with equal enthusiasm, always focused on delivering outcomes, not overhead. 👀 See 8+ Case Studies & Demos: totemxlabs dot com/#casestudies 🎯 Recent Success Stories (2024-2025) 1. Graph RAG for Marketing & Strategic decisions Implemented knowledge graph-powered RAG systems using Neo4j for marketing intelligence and SEO optimization - enabling sophisticated knowledge checks and relationship-based insights that traditional RAG cannot achieve. 2. Workplace Safety Vision System Deployed end-to-end computer vision solution for workplace health and safety compliance - real-time hazard detection, PPE verification, and automated incident reporting. 3. Healthcare Diagnostic Knowledge Graph Developed evidence-based diagnostic system using knowledge graphs for healthcare - producing auditable, traceable diagnostic reasoning paths that meet clinical documentation standards. 🧭 My Proven AI Product Process: Discovery: Identify high-ROI AI opportunities aligned with your goals. Data Assessment: Evaluate data availability, relevance, quality. Solution Design: Define user journeys, tech stacks, evaluation frameworks. Development: Optimized sprints for cost-effective, robust AI solutions (min. tech debt). Deployment: Scalable architectures, rigorous testing for production readiness. 💡 Technical Expertise: GenAI & LLMs: LLM Ops (Fine-tuning, Prompt Engineering, RAG, Agentic Workflow, Multi-agent systems, Memory Management), LangChain, LangGraph, LlamaIndex, LangServe; Vector Search (Pinecone, Weaviate, Chroma, FAISS). AI/ML & MLOps: Predictive Analytics, Recommender Systems, RLHF; Model Lifecycle, MLflow, Feature Stores; PyTorch, TensorFlow, Scikit-learn. Cloud & Infra (Multi-cloud): AWS (Lambda, ECS, ElastiCache, DynamoDB, API Gateway), Azure, GCP, Vertex AI, SageMaker; Docker, Kubernetes (EKS/GKE). Software & Data Eng: Python (expert), Fullstack; FastAPI, Flask, Django; REST, GraphQL, OpenAPI, AsyncIO; Batch/Stream (Airflow, Spark, Kafka), Pandas, Polars, dbt; ETL/ELT. DBs/Arch/DevOps: PostgreSQL, MySQL, DynamoDB, MongoDB, Vector DBs (Pinecone, Weaviate, ChromaDB), Neo4j; Microservices, GitHub Actions, Terraform, CI/CD pipelines. 🤝 Why Partner With Me? I help architect and develop E2E AI solutions merging tech excellence with business acumen. Let's transform the AI wave into your competitive edge. ✅DM me if you have an exciting problem to solve! —Updated January, 2026— Latest LLMs (2025-26): GPT-5.2 (OpenAI's flagship with 26% lower hallucination rate) Claude 4.5 Opus & Sonnet 4.5 (200K context, breakthrough coding performance) Gemini 3 Pro & Flash (Deep Think mode, multimodal excellence, 1M token context) DeepSeek V3.2 & R1 series (685B parameters, hybrid thinking/non-thinking modes) Qwen 3 series (open weight dense and mixture-of-experts (MoE) model for reasoning tasks) Agentic AI Frameworks: Langchain & LangGraph (stateful graph-based workflows, production-grade orchestration) OpenAI Agents SDK (lightweight with guardrails and handoffs) CrewAI (role-based multi-agent collaboration, human-in-the-loop) LlamaIndex Agents (RAG-first agent capabilities) AI workflow automation: n8n Knowledge Graphs & Graph RAG: Neo4j 2025 features: Community summaries, parallel retrievers, LLM Knowledge Graph Builder Neo4j Aura Graph Analytics Graph RAG architecture Cypher query optimization, graph embeddings for ML Agentic knowledge graph construction with provenance Computer Vision Applications: Workplace safety, Video generation Multimodal Vision Language Models (VLMs) Context engineering for high quality content generation

I am a Forward Deployed Engineer with 7+ years of experience embedding directly with AI startups, SaaS companies, and enterprise teams to ship LLM and AI systems from working prototype to production and keep them running reliably as the product scales. Most engagements I join are past the demo stage. The hard part is already in front of the team: messy data, real load, compliance requirements, and systems that need to stay up. That is the environment I work best in. What I have shipped for clients: Designed and deployed a RAG pipeline using Python, FAISS, and Pinecone for a SaaS knowledge base cutting query response time from 4.2s to under 800ms and improving answer relevance scores by 38%. Built a multi-agent AI copilot using LangChain, LangGraph, and GPT-5o, integrated into an enterprise support platform handling 20,000+ user sessions per month. Migrated a fragile notebook-based ML workflow to a production MLOps system on AWS SageMaker and Apache Airflow, reducing model deployment time from 3 days to under 2 hours. Architected a Snowflake data platform for a Series B startup, replacing 6 brittle ETL scripts with a dbt and Airflow pipeline that runs daily with zero manual intervention. Deployed a real-time anomaly detection system on AWS Kinesis and Apache Flink, processing 500k events/day for a fintech client with sub-second alerting. Built a computer vision inspection pipeline using PyTorch and TensorFlow on AWS ECS, reducing manual QA review time by 65% for a manufacturing client. LLM & AI Systems I take LLM products from prototype to production, RAG pipelines with FAISS, Pinecone, and Chroma using hybrid vector and keyword retrieval; multi-agent systems with LangChain and LangGraph; and AI copilots backed by GPT-5o, Claude API, Mistral, and open-source models via HuggingFace and LlamaIndex. For model adaptation I implement LoRA fine-tuning and PEFT to keep inference costs low without sacrificing accuracy. Forward Deployment & Production Engineering I build production inference services using FastAPI and Django, expose REST and GraphQL APIs, and own the full deployment lifecycle inside your environment, not just the model. I embed with your team, learn your systems, and take end-to-end ownership from scoping through go-live. Data Infrastructure I design and build ETL and ELT pipelines with Python, dbt, Apache Airflow, Prefect, and Dagster. For distributed workloads: Apache Spark and PySpark. For streaming: Apache Kafka, AWS Kinesis, Google Pub/Sub, and Apache Flink. Storage across Snowflake, BigQuery, Redshift, ClickHouse, S3 lakehouses, PostgreSQL, MongoDB, and Redis. MLOps & Cloud Full model lifecycle: experiment tracking with MLflow and Weights & Biases, drift detection, and observability pipelines. Infrastructure across AWS (ECS, EKS, SageMaker, Lambda), GCP (Vertex AI, Cloud Run, BigQuery), and Azure ML, containerised with Docker and Kubernetes, deployed via Terraform and GitHub Actions CI/CD, monitored with Prometheus and Grafana. If your LLM prototype needs to become a real product, or your current AI stack is fragile in production, that is exactly where I come in. Let's talk.

I help businesses turn messy, unreliable data into clean, automated, analytics-ready systems. If your data is stuck in spreadsheets, APIs, or broken pipelines, I build reliable ETL workflows that make your reporting and decision-making easy. What I can help you with: • Build end-to-end ETL pipelines (APIs, CSVs, databases) • Design PostgreSQL / BigQuery schemas (star & snowflake) • Automate data ingestion using Python & Airflow • Clean, validate, and transform raw data for BI tools • Optimize slow SQL queries and database performance Tools I work with: Python, SQL, PostgreSQL, BigQuery, dbt, Airflow, AWS, GCP I focus on clear communication, fast delivery, and systems that are easy to maintain after handover. If you need clean data you can trust, let’s talk.