Lead Data Engineering & Lakehouse Architecture

FATI BASIT

Lead Data Engineer • Data Solutions Architect

****.******@*****.*** • 516-***-**** • Katy, Texas 77494 • Open to Remote Opportunities Portfolio: fati-basit.github.io/portfolio

PROFESSIONAL SUMMARY

Data engineering leader with 12+ years building and scaling enterprise data platforms supporting 50B+ healthcare records, millions of daily financial transactions, and mission-critical analytics workloads across healthcare and fintech. Deep hands-on experience across both AWS-native (Redshift, EMR, Glue) and modern Lakehouse stacks (Snowflake, Databricks, Delta Lake, Unity Catalog), with production experience in dbt, Airflow, Kafka, and Spark for batch and streaming pipelines. Practiced in HIPAA-compliant architecture, CI/CD-integrated data quality (Git, automated testing, Great Expectations), and emerging GenAI/RAG workflows using LangChain. Track record of reducing pipeline latency by 65%, delivering $400K+ in annual infrastructure savings, and leading engineering teams to build secure, scalable, business-focused data platforms.

TECHNICAL SKILLS

Programming Python, SQL, PL/SQL, T-SQL, Scala, Java, Golang, .NET Data Engineering ETL/ELT, dbt, Apache Airflow, Azure Data Factory (ADF), SSIS, Informatica, Apache NiFi, Talend, Matillion, Fivetran, Stitch, Prefect, Dagster, Data Migration, Real-Time Data Processing, Pipeline Optimization

Data Warehousing Snowflake, Amazon Redshift, Databricks Lakehouse, BigQuery, Azure Synapse Analytics, Teradata, Oracle, SQL Server, NoSQL, Parquet, ORC, Delta Lake, Apache Iceberg Data Modeling Dimensional Modeling (Star Schema), Data Vault 2.0, Data Mesh, Data Fabric, Semantic Modeling, Data Contracts, Reverse ETL

Big Data & Streaming Apache Hadoop, Apache Spark (Batch & Streaming, PySpark), Apache Kafka, Apache Flink, Apache Beam, Apache Hive, Presto, Apache Storm

Lakehouse Platform Databricks (Unity Catalog, Delta Live Tables, Medallion Architecture, Cluster Performance Tuning), Snowflake (Snowpipe, Cortex, Streamlit-based data apps) MLOps & GenAI MLflow, PyTorch, SageMaker Pipelines, Feast/Feature Store, LangChain, RAG Architectures (document ingestion, chunking, embeddings, retrieval), Vector Databases, LLMOps Cloud Platforms AWS (S3, Glue, Lambda, EMR, Redshift, Kinesis, CloudFormation, Lake Formation, API Gateway, VPC) Azure (Synapse, ADLS Gen2, Azure SQL Database, Azure Data Factory, Event Hub, Azure Functions, Key Vault) • GCP (BigQuery, Dataflow, Composer, Pub/Sub, Vertex AI) DevOps / CI-CD Git, GitHub/GitHub Actions, Docker, Kubernetes, Jenkins, GitLab CI/CD, Azure DevOps, Terraform, Helm, GitOps, Unit, Integration & Acceptance Testing, REST/HTTP API Design Governance &

Security

HIPAA, GDPR, CCPA, PHI, IAM, RBAC, ABAC, Encryption, Data Masking, Data Lineage (OpenLineage, Unity Catalog), Data Cataloging (Alation, Collibra, Purview), Metadata Management Observability &

Quality

Great Expectations, Monte Carlo

BI & Analytics Power BI (DAX, Dataflows), Tableau, Looker, Apache Superset, KPI Modeling, Interactive & Operational Dashboards

Domain Expertise Healthcare (FHIR, HL7, OMOP Common Data Model, Claims, EHR, Eligibility, LOINC, CPT, ICD-10), Finance (Risk, Forecasting), Insurance, Retail, Manufacturing PROFESSIONAL EXPERIENCE

Data Solution Architect January 2022 – Present

Inovalon

• Architected and delivered enterprise-scale healthcare data pipelines processing 50B+ clinical records annually on a hybrid AWS and Snowflake stack, achieving a 65% reduction in end-to-end processing latency and enabling near-real-time analytics for 500+ health plan clients.

• Led adoption of Snowflake alongside the existing Redshift environment for select high-concurrency analytics workloads, using dbt models and Snowpipe for near-real-time ingestion, improving query concurrency for downstream BI consumers.

• Introduced Databricks and Delta Lake for a Medallion Architecture (bronze/silver/gold) supporting ML feature engineering, using Unity Catalog for centralized governance and lineage across both the Databricks and AWS environments.

• Integrated HL7 and FHIR data for secure, HIPAA-compliant healthcare interoperability.

• Designed and enforced HIPAA-compliant data lake architecture with role-based access controls, full audit logging, automated data lineage tracking via Apache Atlas, and governance frameworks covering SLA monitoring, incident response, and data quality SLAs.

• Drove full migration from legacy on-premises ETL to cloud-native ELT using dbt + Apache Airflow, eliminating 40% of pipeline maintenance overhead and enabling the analytics team to deploy new data models 3x faster.

• Established Git-based branching standards and CI/CD pipelines (GitHub Actions) incorporating automated unit and integration tests for dbt models and pipeline code, reducing production defects reaching downstream consumers.

• Led a cross-functional team of 8 data engineers across ingestion, transformation, and data quality domains, defined engineering standards, code review practices, and sprint planning cadences that increased delivery velocity by 30% over two annual cycles.

• Implemented real-time data quality monitoring using Great Expectations integrated into CI/CD pipelines, preventing schema drift and data contract violations from reaching downstream consumers.

• Piloted a Retrieval-Augmented Generation (RAG) proof of concept using LangChain to allow analysts to query clinical documentation and internal data dictionaries in natural language, reducing time spent on manual metadata lookups.

• Delivered executive and operational Power BI dashboards for population health risk stratification, used across all 500+ health plan client accounts.

• Defined infrastructure-as-code standards using Terraform for all production pipeline infrastructure, enabling reproducible deployments, environment parity, and disaster recovery testing across AWS regions. Senior Data Engineer January 2018 – December 2021

Omada Health

• Architected real-time member engagement and risk-scoring pipelines using Apache Kafka and Apache Flink, ingesting live coaching and biometric event streams and reducing time-to-intervention for at-risk program participants from 24 hours to under 5 minutes.

• Built a scalable feature engineering platform on AWS EMR supporting 200+ ML model features for chronic disease risk scoring, enabling the data science team to iterate on clinical outcome models without re-engineering upstream pipelines.

• Designed and implemented a Redshift data warehouse with star-schema dimensional models and optimized materialized views powering executive-level program outcomes dashboards consumed by clinical, product, and finance stakeholders.

• Established a comprehensive data quality framework using Great Expectations with automated alerting and Slack/PagerDuty integration, reducing data incidents by 70% quarter-over-quarter and improving stakeholder trust in reporting accuracy.

• Optimized Spark jobs (PySpark) on AWS EMR through query plan analysis, partition strategy redesign, broadcast join tuning, and cluster right-sizing, delivering $400K+ in annual compute cost savings without degrading pipeline SLAs.

• Partnered with data science teams to productionize ML engagement and risk models, designing low-latency feature serving infrastructure and integrating real-time predictions into coaching workflows.

• Led cross-functional data reviews with clinical and product stakeholders, ensuring alignment on outcome metric definitions, reporting standards, and audit trail requirements across all production pipelines.

• Contributed to the team's shift to a DataOps culture by introducing Git-based workflow standards, automated pipeline testing, and runbook documentation that reduced on-call incident resolution time by 35%. Data Engineer October 2015 – December 2017

Plaid

• Developed and maintained high-throughput ETL pipelines ingesting financial data from 10,000+ banking institutions via REST/HTTP API and SFTP integrations, enforcing schema validation, idempotency, and SLA compliance across all sources.

• Built real-time data ingestion infrastructure on AWS Kinesis processing 2M+ events/day with sub-second latency SLAs, underpinning downstream analytics and customer-facing product features.

• Automated data reconciliation and exception-handling workflows using Python and Apache Airflow, reducing manual data operations effort by 60% and enabling the team to scale institution onboarding without proportional headcount growth.

• Implemented data lineage tracking and metadata cataloging using Apache Atlas, improving data discoverability and reducing mean time-to-debug for pipeline anomalies from days to hours.

• Designed Grafana monitoring dashboards tracking pipeline health, data freshness, throughput metrics, and SLA compliance, providing engineering and product teams with real-time operational visibility across all ingestion workflows.

• Collaborated with product and API teams to onboard new bank integrations, designing resilient connector frameworks that handled API versioning, authentication rotation, and rate-limit backoff strategies using Git-based code review workflows.

• Supported migration of batch ETL pipelines to event-driven architecture on AWS Kinesis and Lambda, improving data freshness from daily batch windows to near-real-time availability. ETL Developer March 2014 – September 2015

Datavant

• Developed Python and SQL-based ETL pipelines for healthcare data tokenization and de-identification, enabling privacy-safe patient record linkage across multiple healthcare organizations in compliance with HIPAA Safe Harbor standards.

• Built automated data validation and monitoring scripts, including unit tests for transformation logic, achieving 99.9% accuracy across patient record linkage workflows and establishing a data quality baseline later adopted as a team-wide standard.

• Designed PostgreSQL schemas and rewrote slow-running analytical queries in partnership with senior engineers, reducing average report generation time by 40% through index optimization and query plan analysis.

• Collaborated with clinical data teams to map source system schemas, resolve data type inconsistencies, and ensure end-to-end HIPAA de-identification compliance, directly supporting client regulatory audits.

• Created internal tooling and developer documentation to streamline onboarding of new healthcare data sources, reducing average integration time from 2 weeks to under 3 days.

• Built an Enterprise Data Warehouse and Reporting Platform on SQL Server, developing SSIS packages and T-SQL stored procedures for ETL processing, with SQL Server Agent-scheduled jobs orchestrating nightly loads and Power BI delivering self-service reporting to business stakeholders.

• Led an On-Premises to Azure Data Migration, re-platforming SQL Server and SSIS-based ETL workloads to Azure SQL Database and Azure Data Factory, using ADLS Gen2 for cloud data storage and Azure Key Vault for secrets and connection-string management, reducing on-premises infrastructure dependency.

KEY PROJECTS

Snowflake & Databricks Lakehouse Modernization Snowflake, Databricks, Delta Lake, Unity Catalog, dbt

• Led a phased migration of high-concurrency analytics workloads from Redshift to Snowflake and introduced a Databricks-based Medallion Architecture for ML feature pipelines, improving query concurrency and unifying governance under Unity Catalog. Real-Time Care Management Engagement Platform Kafka, Apache Flink, Python, AWS EMR, Redshift, Grafana

• Designed and deployed a real-time streaming pipeline processing 5M+ member events/day with ML-integrated risk scoring, reducing time-to-intervention for at-risk participants and improving program engagement outcomes. HIPAA-Compliant Healthcare Data Lake AWS S3, Glue, Redshift, dbt, Airflow, Terraform, Lake Formation

• Architected a cloud-native, fully auditable data lake handling 50B+ clinical records with AWS Lake Formation RBAC, field-level encryption, and Terraform-managed IaC, enabling 500+ health plan clients to access population health analytics with 65% lower query latency.

Clinical Documentation RAG Assistant (Proof of Concept) LangChain, Python, Vector Database, AWS

• Built a Retrieval-Augmented Generation prototype using LangChain and a vector database to let analysts query clinical documentation and data dictionaries in natural language, cutting manual metadata lookup time. Financial Data Ingestion Hub Python, AWS Kinesis, PostgreSQL, Apache Atlas, Airflow

• Built centralized ingestion platform connecting 10,000+ banking institutions, processing 2M+ events/day, automated reconciliation and schema validation reduced data operations manual effort by 60%. On-Premises to Azure Data Migration Azure Data Factory, Azure SQL Database, ADLS Gen2, SSIS, Key Vault

• Re-platformed on-premises SQL Server and SSIS ETL workloads to Azure, using Azure Data Factory for orchestration, ADLS Gen2 for cloud storage, and Key Vault for secrets management, reducing on-premises infrastructure dependency. CERTIFICATIONS

• AWS Certified Data Engineer

• Databricks Certified Associate Developer

• Google Professional Data Engineer

• dbt Analytics Engineering Certification

EDUCATION

Bachelor's in Computer Science 2009 – 2013

University of the Punjab

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