Hire the Best Apache Spark Engineers

I build data platforms that work at scale and keep working as your business grows. Over the past 10 years I've served as the lead or founding data engineer across fintech, e-commerce, ride-hail, legal tech, and cybersecurity companies. That means I've designed systems from scratch, made architecture decisions with no one to fall back on, and delivered platforms that product teams actually use. Here's what I typically get hired to do: → Build greenfield data platforms on AWS or GCP from the ground up → Design and ship production ETL/ELT pipelines (Airflow, Dagster, dbt) → Set up scalable warehouses and governance (Snowflake, BigQuery, Redshift) → Implement real-time streaming pipelines (Kafka, Spark Streaming, CDC) → Build AI-powered data applications (RAG, LLMs, LangChain, vector DBs) → Fix broken or unreliable pipelines and make them production-grade → Architect cloud infrastructure on AWS, GCP, Azure (Terraform, Kubernetes) Recent work includes: - Led data platform engineering for a US e-commerce company processing billions of events daily. I re-architected ingestion pipelines, built Snowflake governance from scratch, introduced Prometheus monitoring and CI/CD standards across the platform. - Built a full data platform on GCP (BigQuery, Dataproc, Airflow) for a music streaming company. Firebase, AppsFlyer, and app store data all flowing into one warehouse within weeks. - Designed an AWS data platform for a ride-hail company managing 500+ streaming and 700+ batch jobs — including a self-serve portal that replaced multi-step CLI workflows for engineers. - Built a legal AI search engine using LangChain, Pinecone, and RAG — full pipeline from document ingestion to LLM-generated answers, deployed on AWS with auto-scaling. - Built an AI inventory insights agent for a US automotive company — multi-source data pipelines, real-time APIs, conversational interface. I work in English daily, communicate proactively, and deliver production- ready code — not prototypes. I'm used to working directly with CTOs and technical leads in US and European time zones. Tools I work with regularly: Python · SQL · Airflow · Dagster · dbt · Snowflake · BigQuery · Spark · Meltano · Kafka · AWS (S3, EMR, Glue, ECS, Lambda, EC2, EKS) · Databricks · GCP · Azure Terraform · Docker · Kubernetes · LangChain · FastAPI · MLflow · Weaviate, Celery If you're building a data platform, fixing one, or adding AI/ML capabilities to your stack, let's talk.

You probably think clicking "deploy" on Databricks from the cloud marketplace is all it takes to build a modern data stack. Instead, you get unmanageable infrastructure, skyrocketing costs, and pipelines feeding reports nobody trusts. 𝗜 𝗳𝗶𝘅 𝘁𝗵𝗮𝘁. 𝗡𝗼 𝗮𝗴𝗲𝗻𝗰𝗶𝗲𝘀, 𝗻𝗼 𝗯𝗹𝗼𝗮𝘁. Just a multi-certified, 5+ years of experience Cloud Solutions Architect building automated, high-integrity platforms that turn raw data into a competitive advantage. If you shoot me a invitation or message I'll send you a personalized Loom video back on how I may be able to help you; and of course, to prove that I'm the real deal, 𝗻𝗼 𝗔𝗜 𝗶𝗻𝘃𝗼𝗹𝘃𝗲𝗱! Whether you are building a greenfield lakehouse from scratch or migrating legacy systems to the cloud, I architect efficient, cost-effective environments that scale without the overhead. I understand the business bottom line just as well as the underlying code. ✪ 100% Job Success Score | 5.0★ average ✪ Proven experience on multi-cloud architectures 💡 𝗪𝗵𝗮𝘁 𝗜 𝗱𝗼: • 𝗗𝗮𝘁𝗮 𝗣𝗹𝗮𝘁𝗳𝗼𝗿𝗺 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴: I build production-ready environments using Terraform. No manual marketplace or standard deployments that break at scale. • 𝗥𝗲𝗹𝗶𝗮𝗯𝗹𝗲 𝗗𝗮𝘁𝗮 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴: Raw data becomes actionable. I build resilient Medallion architectures and automated ETL/ELT pipelines so your stakeholders actually trust the numbers. • 𝗣𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻 𝗠𝗟𝗢𝗽𝘀: I bridge the gap between data engineering and machine learning. Using MLflow and Databricks Model Serving, I operationalize models into scalable, real-time REST endpoints and automated streaming inference pipelines. • 𝗚𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲 & 𝗦𝗲𝗰𝘂𝗿𝗶𝘁𝘆: Proper data governance utilizing Unity Catalog (no legacy Hive metastores) to ensure your data is accessible, secure, and future-proof. • 𝗖𝗹𝗼𝘂𝗱 𝗖𝗼𝘀𝘁 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻: Most companies overspend on cloud infrastructure. I architect systems that pay for themselves in weeks by eliminating overhead and inefficiencies with efficient auditing and monitoring features. ✅ 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻𝘀 (𝘃𝗲𝗿𝗶𝗳𝗶𝗲𝗱): • Databricks Professional Data Engineer • Databricks Associate Data Engineer • Databricks Lakehouse Fundamentals • GCP Professional Data Engineer • GCP Associate Cloud Engineer • GCP Cloud Digital Leader • AWS Associate Solutions Architect • AWS Cloud Practitioner 🔧 𝗘𝘅𝗽𝗲𝗿𝗶𝗲𝗻𝗰𝗲 𝘄𝗶𝘁𝗵 𝗖𝗹𝗼𝘂𝗱 𝗦𝗲𝗿𝘃𝗶𝗰𝗲𝘀: • 𝗗𝗮𝘁𝗮𝗯𝗿𝗶𝗰𝗸𝘀: Workflows, LDP (Lakeflow Declarative Pipelines), Unity Catalog, Workflows, Databricks SQL, MLFlow. • 𝗔𝗺𝗮𝘇𝗼𝗻 𝗪𝗲𝗯 𝗦𝗲𝗿𝘃𝗶𝗰𝗲 (𝗔𝗪𝗦): EMR, Athena, Redshift, Glue, S3, RDS, Kinesis Data Firehose, Kinesis, and Data Streams. • 𝗚𝗼𝗼𝗴𝗹𝗲 𝗖𝗹𝗼𝘂𝗱 𝗣𝗹𝗮𝘁𝗳𝗼𝗿𝗺 (𝗚𝗖𝗣): Bigquery, Dataform, Composer, Dataflow, Dataproc, Cloud Storage, Pub/Sub, Cloud Functions, and Looker Studio. • 𝗠𝗶𝗰𝗿𝗼𝘀𝗼𝗳𝘁 𝗔𝘇𝘂𝗿𝗲: Data Factory, Synapse, and Storage Account. • 𝗢𝘁𝗵𝗲𝗿𝘀: Terraform, dbt, Airflow, Airbyte, Hadoop, and Hive. ⚙️ 𝗖𝗼𝗿𝗲 𝗲𝘅𝗽𝗲𝗿𝘁𝗶𝘀𝗲: • 𝗥𝗼𝗹𝗲𝘀: Data Architect, Data Engineer, Solutions Architect, Platform Engineer • 𝗣𝗹𝗮𝘁𝗳𝗼𝗿𝗺𝘀: Databricks (Delta Lake, Unity Catalog, Lakeflow, Workflows), BigQuery • 𝗜𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲: Infrastructure as Code (IaC), Terraform, Multi-Cloud (AWS, GCP, Azure) • 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲: Medallion Architecture, Data Lakehouse, Data Governance, Data Quality, Machine Learning • 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴: PySpark, Python, SQL, dbt, Apache Airflow, ETL/ELT, CDC, Batch and Stream Processing

With 7+ years of experience, I'm Expert in Web Scraping, Data Engineer, AI/ML and Full-Stack Developer specializing in large-scale data extraction, automation, and pipeline engineering. I build robust, scalable systems that transform raw data into actionable insights. 💡 Core Expertise Web Scraping & Automation: Expert in bypassing anti-bot systems (CAPTCHA, rate limits, IP rotation) using Scrapy, BeautifulSoup, Selenium, Playwright, and rotating proxies. Automation & Workflow Engineering: Airflow, Prefect, Dagster, n8n, Zapier, Make, Power Automate, UiPath, Step Functions, Logic Apps, GCP Workflows, Business Process Automation, RPA, CI/CD, Jenkins, GitHub Actions, GitLab CI/CD, Monitoring & Alerting. Data Engineering: Designing and building scalable ETL/ELT pipelines for structured, semi-structured, and unstructured data using Apache Airflow, Apache Spark (PySpark), Pandas, Dask, Databricks, Snowflake, Apache Kafka, Apache Hive, Apache Hadoop, Delta Lake, Apache Iceberg, dbt, AWS Glue, Azure Data Factory, Google Cloud Dataflow, Apache NiFi, Trino, Presto, and Apache Beam. Experienced in data warehousing, data lakes, lakehouse architectures, data modeling, data transformation, data quality, data governance, batch and real-time processing, streaming data pipelines, orchestration, workflow automation, schema design, partitioning, optimization, and performance tuning. Proficient with cloud platforms including AWS, Azure, and GCP, S3, Redshift, EMR, Athena, Lambda, Azure Synapse Analytics, Azure Data Lake Storage, BigQuery, Cloud Storage, and Pub/Sub. Skilled in SQL, Python, data integration, data migration, CDC, metadata management, monitoring, CI/CD, Docker, Kubernetes, and modern data stack technologies. Backend Development: High-performance APIs and microservices with FastAPI, Django, Flask, and Celery for async task handling. AI/ML Integration: Leveraging NLP and LLMs (LangChain, Llama, NLTK) for data enrichment, classification, and intelligent automation. Cloud & DevOps: Deploying scalable scrapers and data workflows on AWS (Lambda, ECS, S3), GCP, Docker, and Kubernetes. 🛠️ Tech Stack Data & Scraping: ▸ Scrapy | Selenium | Playwright | Proxies (BrightData, ScraperAPI, etc) ▸ Pandas | PySpark | Apache Airflow | PostgreSQL | MongoDB | Redis Backend & Cloud: ▸ Python (FastAPI, Django, Flask) | Celery | RabbitMQ ▸ AWS (Lambda, ECS, RDS, S3) | GCP | Docker | Kubernetes AI/ML: ▸ NLP (NLTK, spaCy) | LLMs (LangChain, OpenAI, Llama) | Data Annotation Let's turn your data challenges into reliable, scalable solutions. Send me a message to discuss your project!

A Senior Data Engineer with 8+ years of experience building reliable, scalable data pipelines and infrastructure, from data ingestion and transformation through warehousing, streaming, and data analytics with dbt, Snowflake, Airflow across AWS, Azure, and GCP with robust ETL and ELT. If your data pipelines are brittle, your data warehouse is slow, or your data was never built to scale, that is exactly what I fix, with fault tolerance, observability, and audit-ready quality engineered in from day one. I cover the full data engineering lifecycle: batch and real-time data pipelines, Modern Data Stack builds, lakehouse architecture, cloud and warehouse data migration, governance, and the data foundations that feed modern systems. 🎯 Core Expertise: ✅ Data Pipelines & Orchestration: End-to-end batch and real-time pipelines with Apache Airflow, Dagster, Prefect, and Azure Data Factory. Idempotent, schema-drift tolerant, and monitored so failures surface before they reach your stakeholders. ✅ Cloud Warehousing & Lakehouse: Snowflake, BigQuery, Amazon Redshift, Databricks, and Microsoft Fabric, with Delta Lake and Apache Iceberg lakehouse foundations, Medallion Architecture, partitioning, and performance tuning. ✅ Data Transformation & Modeling: dbt (Core and Cloud), SQLMesh, Spark and PySpark, Star Schema and dimensional modeling, analytics engineering best practices, full test coverage, and CI/CD for data models. ✅ Streaming & Real-Time Analytics: Distributed streaming with Apache Kafka, Flink, Spark Structured Streaming, Kinesis, and Pub/Sub, including exactly-once semantics, dead-letter queues, CDC, and end-to-end latency guarantees. ✅ Data Ingestion & Integration: Fivetran, Airbyte, Matillion, Stitch, Hevo, Meltano, and custom CDC pipelines for near-real-time sync across structured, semi-structured, and unstructured sources. ✅ Data Quality, Governance & Observability: Automated data quality frameworks, SLA monitoring, auditable lineage, data catalog and metadata management, and observability that catches bad data early. ✅ Cloud Migration & Modernization: Zero-downtime migration handled end to end, from legacy warehouse assessment through cutover, with zero data loss and minimal downtime, replacing brittle ETL and ELT with a clean Modern Data Stack. ✅ AI-Ready Data Infrastructure: Pipelines engineered to feed LLMs and ML systems with clean, structured, high-quality data, from ingestion through transformation to serving. ------------------------------------------------------ ⚙️Tech Stack: ⚡ Warehouses & Lakehouse: Snowflake | BigQuery | Redshift | Databricks | Microsoft Fabric | Delta Lake | Iceberg ⚡ Transformation: dbt | SQLMesh | Spark | PySpark | Star Schema | Medallion Architecture ⚡ Orchestration: Airflow (GCP Cloud Composer and AWS MWAA) | Dagster | Prefect | Azure Data Factory ⚡ Streaming: Kafka | Flink | Kinesis | Pub/Sub | Spark Structured Streaming | ClickHouse ⚡ Ingestion: Fivetran | Airbyte | Matillion | Stitch | Hevo | Meltano | CDC ⚡ Cloud: AWS | GCP | Azure ⚡ Languages: Python | SQL (Snowflake, BigQuery, T-SQL, PL/pgSQL) | FastAPI ⚡ Databases: PostgreSQL | MySQL | SQL Server | DynamoDB | MongoDB ⚡ BI & Reporting: Looker | Tableau | Power BI | GA4 | Metabase | Superset | Streamlit | Grafana ------------------------------------------------------ ⭐ What Clients Say: 🏅 "Adarsh rebuilt our analytics pipeline on Snowflake, Airflow, and dbt, giving us reliable, version-ready data. Reporting accuracy improved overnight, and we can finally trust the numbers." – Anita, Head of Product, FinTech SaaS 🏅 "He designed a zero-downtime migration to a modern data warehouse that cut query latency by more than half while keeping our SLAs intact." – Daniel, VP of Data, AdTech Firm 🏅 "Clean architecture, solid dbt models, and Airflow pipelines running without issues for months. He brought a level of engineering discipline we hadn't seen from a data consultant before." – Mark, Director of Data Engineering, E-commerce Startup 🏅 "We came to him with a Spark pipeline costing us a fortune and delivering stale data. He restructured the workflow logic and cut processing time by 70%." – Leo, Head of Analytics, HealthTech SaaS ------------------------------------------------------ 🏆 TOP RATED PLUS | EXPERT-VETTED | Top 1% on Upwork | 8+ Years Experience | 100% Job Success 🚀 Ready to build a scalable, production-ready data infrastructure to turn your raw data into reliable, actionable business insights? Click the 'Invite to Job' button on the top right, and let's discuss your data pipeline!

𝐒𝐞𝐧𝐢𝐨𝐫 𝐃𝐚𝐭𝐚 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫 | 𝐏𝐲𝐭𝐡𝐨𝐧 · 𝐒𝐩𝐚𝐫𝐤 · 𝐒𝐧𝐨𝐰𝐟𝐥𝐚𝐤𝐞 · 𝐀𝐢𝐫𝐟𝐥𝐨𝐰 · 𝐊𝐚𝐟𝐤𝐚 | 𝐀𝐈/𝐌𝐋 𝐃𝐚𝐭𝐚 𝐈𝐧𝐟𝐫𝐚𝐬𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐞 | 7+ 𝐘𝐞𝐚𝐫𝐬 I build the data infrastructure that powers AI systems at scale. If your pipelines are slow, brittle, or can't support the ML/AI workloads you're trying to run that's the problem I solve. 7+ years building production data systems across FinTech, HealthTech, E-commerce, Trading Platforms, and B2B SaaS. Not just pipelines that move data pipelines that are reliable, observable, and designed to support machine learning and GenAI workflows from day one. ━━━━━━━━━━━━━━━━━━━━━━ 𝐂𝐨𝐫𝐞: 𝐃𝐚𝐭𝐚 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐢𝐧𝐠 ━━━━━━━━━━━━━━━━━━━━━━ → Scalable ETL/ELT pipeline design and development → Batch and real-time streaming architectures (Spark, Kafka, Airflow) → Data warehouse design and optimization (Snowflake, BigQuery, Redshift) → Data lake and lakehouse architecture (Delta Lake, Databricks) → Data modeling, schema design, and query performance tuning → Data quality frameworks validation, monitoring, and lineage tracking → dbt transformations and modular data modeling → Cloud data platform engineering (AWS, GCP, Azure) ━━━━━━━━━━━━━━━━━━━━━━ 𝐃𝐢𝐟𝐟𝐞𝐫𝐞𝐧𝐭𝐢𝐚𝐭𝐨𝐫: 𝐀𝐈/𝐌𝐋 𝐃𝐚𝐭𝐚 𝐈𝐧𝐟𝐫𝐚𝐬𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐞 ━━━━━━━━━━━━━━━━━━━━━━ Most data engineers build pipelines. I build pipelines that are AI-ready. → Embedding pipelines and vector store ingestion for RAG systems → Feature engineering pipelines for ML model training and serving → Scheduling isolation so AI workloads don't starve core ETL → MLOps data layer experiment tracking, model versioning, data lineage → Production data infrastructure for LLM and GenAI applications ━━━━━━━━━━━━━━━━━━━━━━ 𝐖𝐡𝐚𝐭 𝐂𝐥𝐢𝐞𝐧𝐭𝐬 𝐇𝐢𝐫𝐞 𝐌𝐞 𝐅𝐨𝐫 ━━━━━━━━━━━━━━━━━━━━━━ → "Our pipelines break and nobody knows why" I build observable, reliable pipelines with alerting and lineage → "We need a data layer that supports our AI product" I design the infrastructure from ingestion to serving → "Our Snowflake/BigQuery costs are out of control" I optimize queries, schemas, and partitioning → "We're moving from batch to real-time" I architect Kafka + Spark streaming systems → "Our data team can't support the ML team's needs" I bridge that gap ━━━━━━━━━━━━━━━━━━━━━━ 𝐒𝐭𝐚𝐜𝐤 ━━━━━━━━━━━━━━━━━━━━━━ 𝐃𝐚𝐭𝐚 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐢𝐧𝐠 Python · SQL · Apache Spark · PySpark · Apache Kafka · Apache Airflow · dbt · Snowflake · BigQuery · Redshift · Delta Lake · Databricks 𝐂𝐥𝐨𝐮𝐝 & 𝐈𝐧𝐟𝐫𝐚𝐬𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐞 AWS (S3, Glue, Redshift, SageMaker) · GCP (BigQuery, Dataflow, Vertex AI) · Azure (Data Factory, Synapse) · Docker · Kubernetes 𝐀𝐈/𝐌𝐋 𝐃𝐚𝐭𝐚 𝐋𝐚𝐲𝐞𝐫 MLflow · Weights & Biases · pgvector · Pinecone · LangChain · Feature Stores · Embedding Pipelines 𝐀𝐏𝐈𝐬 & 𝐁𝐚𝐜𝐤𝐞𝐧𝐝 FastAPI · Flask · REST APIs · Python scripting ━━━━━━━━━━━━━━━━━━━━━━ 𝐈𝐧𝐝𝐮𝐬𝐭𝐫𝐢𝐞𝐬 ━━━━━━━━━━━━━━━━━━━━━━ FinTech · HealthTech · E-commerce · Retail Tech · Stock Trading · Logistics · B2B SaaS

Messy data slowing your team down? I build scalable ETL/ELT pipelines and modern cloud architectures on Azure, Databricks, Fabric, and Snowflake that turn raw, chaotic data into clean, analytics-ready systems fast and reliably. I bridge the gap between fragmented data sources and production-grade dashboards, seamlessly adapting to your existing infrastructure rather than forcing an expensive rebuild. What I Can Help You With: Data Warehouse & Lakehouse Architecture: Implementing Medallion design patterns (Bronze → Silver → Gold) using Delta Lake, Microsoft Fabric OneLake, and Snowflake. Scalable ETL/ELT Ingestion: Building automated, metadata-driven pipelines via Azure Data Factory, Fabric Pipelines, Databricks (PySpark/SQL), and dbt. Real-Time Data Streaming: Architecting low-latency workflows using Apache Kafka, Azure Event Hubs, and streaming engines. Database Design & Optimization: Performance tuning, indexing, and data modeling for PostgreSQL, Azure SQL, and cloud warehouses. Proven Project Highlights: Microsoft Fabric Incremental Pipeline: Built a control-table pattern using Get Metadata, Lookup, and ForEach loops to orchestrate zero-duplicate, quarterly ingestion from SharePoint into OneLake via Dataflow Gen2. Azure/Databricks Streaming: Developed a restaurant analytics platform processing 80,000+ events/day, cutting reporting lag from 6 hours to under 3 minutes. Kafka/Snowflake Pipeline: Engineered a real-time stock market data pipeline tracking 120+ tickers with under 8 seconds end-to-end latency. I write clean, documented code your team can maintain long-term and provide transparent daily updates. Message me with your data challenge and I’ll walk you through exactly how to solve it.