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  Data Engineer with 7+ years building production-grade data platforms and Lakehouse architectures across banking, healthcare, and telecom, leading work end to end from requirements and design through release validation and production support.
  Implemented Azure and AWS Lakehouse platforms end to end using Azure Databricks, Delta Live Tables, Unity Catalog, Auto Loader, and Medallion Architecture, establishing reusable pipeline and data modeling standards teams could build on confidently.
  Built multi-cloud ETL and ELT pipelines across Azure (Data Factory, Databricks, ADLS Gen2, Synapse Analytics, Microsoft Fabric), AWS (Glue, EMR, Lambda, Step Functions, Redshift, Athena), and GCP (BigQuery), handling CDC, schema evolution, late-arriving data, and backfill strategies.
  Developed Python and Scala backend services with REST APIs, GraphQL, OAuth2/JWT authentication, PostgreSQL state management, and Redis caching, owning production observability through Datadog monitoring, incident response, and postmortems.
  Orchestrated complex workflows using Apache Airflow, Databricks Workflows, and AWS Step Functions, built governed ELT models using dbt and Snowflake, and partnered with analytics and business teams to define data contracts, data catalogs, and reduce recurring ad hoc requests.
  Established data quality and governance programs using Delta Live Tables Expectations, Great Expectations, and Soda across HIPAA, SOX, and GDPR-regulated pipelines for audit readiness, data lineage, and regulatory compliance.
  Translated ambiguous business requirements into well-scoped engineering deliverables, communicated progress clearly to finance, compliance, and clinical stakeholders, and managed competing priorities effectively across Agile sprint cycles.
  Set pipeline, data modeling, and coding standards in PySpark, Scala, Python, and SQL, mentored engineers at different levels, conducted code reviews, and drove CI/CD through GitHub Actions, Azure DevOps, and Terraform.


Innovative Enterprise Data Architect known for productivity and efficiency in task completion. Possess specialized skills in data modeling, cloud architecture, and big data analytics. Excel in communication, problem-solving, and strategic planning, ensuring successful project outcomes and team collaboration.

Accomplished Data Engineer with a proven track record at Clover, specializing in ETL optimization and cloud data migration. Expert in Python and Apache Airflow, I enhanced system stability and reduced downtime, driving significant improvements in data accessibility and processing efficiency. Strong collaborator, adept at delivering impactful solutions in fast-paced environments.

I am a highly skilled Data Engineer with over 11 years of experience specializing in AWS and GCP-based big data solutions. I have a strong track record of designing and implementing scalable ETL/ELT pipelines, real-time data streaming architectures, and cloud-native data platforms across industries such as finance, insurance, telecommunications, and healthcare. My technical expertise spans a wide range of cloud services and tools including AWS Glue, Redshift, Lambda, EMR, Kinesis, Terraform, Snowflake, Google Cloud Dataflow, BigQuery, and Cloud Composer (Apache Airflow). I have also worked extensively with dbt (Data Build Tool) to build modular, testable data transformations and ensure data quality in modern data stack environments. I bring deep experience in developing high-performance data lakes, orchestrating pipelines, managing data governance, and deploying infrastructure as code. My work emphasizes cost-efficiency, performance optimization, and compliance with regulatory standards (HIPAA, GDPR, SOC 2, PCI-DSS). Proficient in Python, SQL, Apache Spark, and Apache Beam, I thrive in fast-paced, collaborative environments and enjoy solving complex data challenges. I am passionate about enabling data-driven decision-making through robust, scalable, and secure data solutions built on cutting-edge cloud and big data technologies.

KHALID LAKSANTINI

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