Open to opportunities

Haz Khalid

@hazkhalid

Staff-level data engineer and cloud data platform architect specializing in scalable lakehouse and real-time ML pipelines.

United States

Message

What I'm looking for

I’m looking to lead cloud data platform and lakehouse initiatives—building scalable batch/streaming pipelines, enforcing governance and observability, and shipping real-world ML workflows with measurable performance, reliability, and cost optimization.

I’m a seasoned Data Engineer and Cloud Data Platform Architect with 9+ years of experience designing and implementing scalable, high-performance data systems across AWS, GCP, and Azure. I lead end-to-end data engineering—from enterprise data platform and modern lakehouse architectures to reliable batch and streaming ETL/ELT pipelines.

I build platforms that teams can trust: I establish data quality, governance, and compliance aligned with SOC2, HIPAA, GDPR, and CCPA, and I strengthen observability with Prometheus, Grafana, and CloudWatch. I also drive real-world impact through infrastructure-as-code with Terraform, containerization with Docker and Kubernetes, and CI/CD automation—plus ML pipeline and model deployment workflows using MLflow, SageMaker, and VertexAI.

Experience

Work history, roles, and key accomplishments

Current

Data Platform Architect

Current

Slickdeals

Jan 2023 - Present (3 years 3 months)

Led architecture and development of enterprise data platforms and modern lakehouse architectures supporting large-scale analytics and ML workloads. Designed scalable ETL/ELT pipelines with PySpark, SQL, Kafka, and Airflow, and implemented multi-cloud infrastructure with governance, security/compliance, and Prometheus/Grafana/CloudWatch observability.

Pyspark SQL Kafka Airflow Databricks Delta Lake Terraform Kubernetes Prometheus Grafana

Machine Learning Data Engineer

Fivetran

Jan 2020 - Jan 2023 (3 years)

Designed and implemented scalable data pipelines and ML feature-engineering workflows using Spark, Databricks, Airflow, and Snowflake. Optimized large-scale workloads on Databricks, BigQuery, and Redshift, improving pipeline performance by 30%, and implemented monitoring, data validation, and lineage tracking.

Apache Spark Databricks Airflow Snowflake Kafka DBT ELT BigQuery Redshift Data Lineage

Cloud Data & AI Engineer

Mozrat Data

Jan 2017 - Jan 2020 (3 years)

Developed cloud-native ETL pipelines and data lake architectures across AWS, Azure, and GCP. Built real-time ingestion pipelines with Kafka, Spark Streaming, and Apache Flink, deployed containerized data/ML services with Docker and Kubernetes, and established monitoring/logging using Prometheus, Grafana, and ELK stack.

AWS GCP Kafka Apache Flink Docker Kubernetes Prometheus Grafana

Data Platform Engineer

Nexla

Jan 2015 - Jan 2017 (2 years)

Built foundational ETL pipelines and batch data-processing systems using Python, SQL, and Apache Spark. Designed ingestion workflows integrating multiple data sources into enterprise data lakes and warehouses, optimized processing performance across Hadoop-based distributed systems and SQL warehouses, and implemented automated data workflows with data quality checks.