Ehmad Aslam

Results-driven Data Engineer with 7+ years of experience building and scaling production-grade data platforms across ecommerce and logistics SaaS domains. Deep expertise in Python, SQL, dbt, Databricks, and PySpark, with a strong command of modern data stack tooling including Snowflake, Airflow, Dagster, and Airbyte. Proven ability to design and deliver high-throughput ELT pipelines, real-time streaming architectures, and cloud-native data warehousing solutions that power analytics at scale.

Certified in Databricks and dbt. Consistently delivers clean, maintainable data infrastructure that directly drives business outcomes.

Data Engineer at Reveel Group (2022-07 – Present)

Owned the Databricks Lakehouse on Azure (ADLS Gen2 Bronze → Silver → Gold), giving 100+ enterprise clients a single trusted source for shipping spend, audit, and performance analytics.

• Built a generic, cursor-based API ingestion library for UPS, FedEx, DHL, and similar carriers; published to Azure DevOps Artifacts and cut time to onboard new carrier sources by standardizing ingestion across platforms.
• Ingested 100M+ parcel invoice records per month from carrier APIs and EDI, enabling automated parcel audit and recovery that reduced manual invoice review and improved client cost recovery.
• Used checkpoint-based incremental loads into ADLS Gen2, so failed runs recovered without duplicates or gaps and kept downstream billing and audit workflows reliable.
• Ran Databricks Autoloader pipelines from Bronze to Silver Delta tables with sub-2-hour freshness, so client-facing shipping analytics reflected near-current carrier data.
• Migrated legacy SQL to 200+ tested dbt models on the Lakehouse, cutting pipeline failures by 40% and shortening feature delivery by 30% for product and analytics teams.
• Built PySpark jobs on hundreds of millions of contract rows for Modeling & Simulation, helping enterprise clients forecast shipping costs and compare carrier contract scenarios before signing.
• Built the Reveel Peer Index with Spark and dbt, giving clients shipping health benchmarks against peers and supporting retention and upsell conversations.
• Designed star-schema Gold models for self-serve BI across spend management and shipping analytics, reducing ad hoc data requests to the engineering team.
• Implemented Unity Catalog governance, access controls, and lineage on Lakehouse tables, reducing unauthorized access risk and speeding compliance reviews for finance and invoice data
• Rolled out dbt tests and Great Expectations, cutting production data incidents by 60% and preventing bad carrier or invoice data from reaching client reports.
• Tuned Spark jobs with partitioning, Delta optimization, and cluster autoscaling, reducing average runtime by 45% and lowering compute cost on monthly carrier processing.
• Defined data contracts, pipeline SLAs, and monitoring with product and engineering so client-facing datasets had clear ownership, freshness targets, and alert coverage.
• Mentored junior engineers on Databricks, API ingestion, dbt, and PySpark, improving team delivery speed and reducing rework on production pipelines

Junior Data Engineer at Spreetail (2019-01 – 2022-06)

Ecommerce accelerator · multi-marketplace fulfillment · 20+ channels · big-and-bulky logistics

• Built Kafka-based streaming pipelines to ingest high-volume order and inventory events from 20+ marketplaces (Amazon, Walmart, Target), giving operations teams live visibility into fulfillment across Spreetail's warehouse network.
• Supported same-day fulfillment SLA tracking and operational alerting by feeding near-real-time order and inventory data to ops teams, helping protect Spreetail's high same-day ship performance for brand partners.
• Designed Snowflake data models to consolidate order, inventory, and fulfillment data across all marketplace channels into one analytical layer for operations, merchandising, and analytics teams.
• Developed dbt transformation models on Snowflake for marketplace performance reporting, seller metrics, and fulfillment KPIs—including same-day ship rate, cancellation rate, and inventory turn—used in partner reviews and internal performance tracking.
• Orchestrated pipelines with Dagster, building software-defined assets for order ingestion, inventory sync, and marketplace fee reconciliation with built-in lineage and observability to reduce pipeline failures and speed troubleshooting.
• Configured and maintained Airbyte connectors to extract data from 10+ marketplace and logistics APIs into S3, standardizing raw ingestion and cutting setup time when adding new sales or fulfillment data sources.
• Built Python event consumers on Kafka topics for real-time inventory availability, feeding Snowflake tables that powered Promise Pro smart shelf and accurate delivery promise logic at checkout.
• Designed S3-based data lake landing zones with partitioning by marketplace, date, and event type, making downstream Snowflake loads and historical reporting faster and cheaper to run.
• Partnered with analytics and operations teams to deliver dashboards and data models covering $150M+ in managed ad spend and multi-marketplace revenue attribution, helping merchants improve ROAS and channel mix decisions for brand partners.
• Improved data reliability for partner-facing reporting by adding dbt tests and validation checks on key marketplace and fulfillment tables, reducing bad metrics in recurring merchant performance reviews.
• Supported multi-channel listing and catalog health analytics by modeling product, inventory, and order data so teams could monitor availability and sales performance across Spreetail's global marketplace footprint.

Bachelor of Science in Computer Science – University of Science and Technology (NUST) (2014 – 2018)