Aqeel A.Private profile: Upwork shows its earnings only to signed-in members, so we check it every few weeks, not daily. MRR here is a rough estimate (Upwork's six-month earnings ÷ months active). Last read Aug 28, 2026.

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