Senior Data Engineer
Resume:
Results-driven Senior Data Solution Architect with 10+ years of
experience designing and optimizing scalable data platforms, ETL pipelines, and data warehouses. Proven expertise in big data technologies including Hadoop, Spark, Kafka, and real-time stream processing. Skilled in building cloud-native architectures on AWS, Azure, and GCP, integrating data solutions with tools like Talend, Apache NiFi, Informatica, and Airflow.
Experienced in deploying machine learning models using TensorFlow, Scikit-learn, and PyTorch within Databricks and MLOps frameworks. Strong background in data governance, quality management, and regulatory compliance (HIPAA, GDPR). Adept at translating business requirements into actionable insights using Tableau, Power BI, and QuickSight. Known for delivering secure, scalable, and high-performance solutions across finance, healthcare, and enterprise domains.
Professional Experience
Senior Data Engineer
CoreLogic
2021-07 -
Current
Designed enterprise-wide data pipelines using
Spark, Airflow, and Python, processing 10+ TB of
data daily.
•
Migrated legacy data warehouse into Snowflake,
reducing infrastructure costs by 25% and
improving performance by 45%.
•
Implemented real-time event-driven pipelines
using Kafka + Flink to support real-time decision- making.
•
Led a team of 6 engineers, establishing best
practices for CI/CD, testing, and monitoring of
data pipelines.
•
Defined data governance policies (Apache Atlas
+ Collibra) to ensure compliance with GDPR and
CCPA.
•
• Data pipeline design & optimization: Builds and
Contact
Address
Los Angeles, CA 90001
Phone
************@*****.***
Skills
• Code automation
Python automation and
API integration
•
Data integration
expertise
•
Data quality
management
•
Data warehousing
expertise
•
Proficient in Azure data
solutions
•
Apache Kafka, Amazon
Kinesis, Apache Flink,
and Google Pub/Sub.
•
• CI/CD & DevOps
Tableau, Power BI,
Looker, and Apache
Superset.
•
• Python programming
Senior Data Engineer Big Data Engineer Data Warehouse Engineer
Rana Jalil
maintains ETL/ELT pipelines to move data from
multiple sources into warehouses, lakes, or
analytics platforms.
Architecture & scalability: Designs systems that
can handle growing data volumes efficiently.
•
Cloud & big data tools: Works with technologies
like AWS, Azure, GCP, Spark, Kafka, Hadoop,
Databricks, Snowflake, etc.
•
Data modeling: Structures data for usability,
performance, and accuracy.
•
Collaboration: Works with data scientists, analysts, and business stakeholders to understand needs
and deliver solutions.
•
Mentorship: Provides guidance to junior engineers
and ensures best practices in coding, security,
and performance.
•
Big Data Engineer
Effectual
2017-06 -
2021-06
Designed and implemented real-time data
pipelines using Kafka, Spark Streaming, and AWS
Kinesis to process over 5TB+ of daily data.
•
Migrated large on-premise Hadoop clusters to
AWS EMR and Google Dataproc, reducing
infrastructure costs by 30%.
•
Built data lake architecture on AWS S3 with
metadata management using Glue Catalog and
Apache Hive.
•
Partnered with machine learning teams to deliver
feature engineering pipelines using PySpark and
Delta Lake.
•
Optimized Hive and Spark jobs, reducing query
runtimes by 40% through partitioning, bucketing,
and caching strategies.
•
Established data governance, lineage tracking,
and security policies with Apache Atlas and AWS
Lake Formation.
•
Data ingestion: Builds systems to capture and
integrate data from multiple, high-speed sources.
•
Storage management: Designs and manages
large-scale data storage solutions such as
Hadoop HDFS, Amazon S3, or distributed file
systems.
•
Processing frameworks: Works with big data tools
like Apache Spark, Hadoop, Flink, Hive, or Kafka
•
• Data pipeline design
• ETL development
• Advanced SQL
Projects
Healthcare Predictive
Analytics Platform:
Python, Apache Spark,
AWS S3, Redshift,
Scikit-learn
•
Real-time Customer
Behavior Tracking:
Apache Kafka, Spark
Streaming, AWS Kinesis,
PostgreSQL, End-to-End
Data
•
Financial Data:
Snowflake, AWS Glue,
Apache Airflow, Python
•
Certificates
Databricks Certified
Associate Developer for
Apache Spark:
Informatica Cloud Data
Integration Specialist:
Microsoft Certified: Azure AI
Engineer Associate:
Education
2014-01
Bachelor of Science:
Computer Science
Punjab University
to process and transform huge datasets.
Data pipeline development: Creates scalable
ETL/ELT pipelines to move and prepare data for
analytics and machine learning.
•
Performance optimization: Ensures systems can
handle petabytes of data efficiently with high
availability and fault tolerance.
•
Collaboration: Partners with data scientists,
machine learning engineers, and analysts by
preparing data that supports advanced analytics
and AI.
•
Data Warehouse Engineer
Databridge Analytics
2014-07 -
2017-05
Designed and implemented enterprise data
warehouse in Snowflake, supporting 1,000+
business users.
•
Developed ETL pipelines with dbt + Airflow,
automating ingestion and transformation
workflows.
•
Optimized queries and models, improving
dashboard load times by 60%.
•
Collaborated with BI teams to deliver KPI
dashboards in Power BI and Tableau.
•
Defined data modeling standards (Kimball &
Data Vault) to ensure scalability and
maintainability.
•
Data integration: Designs ETL/ELT processes to pull data from diverse sources and load it into the
warehouse.
•
Data modeling: Structures the warehouse using
schemas (star, snowflake, or normalized) to make
querying efficient and intuitive.
•
Performance optimization: Ensures queries run
fast and the warehouse can scale as data
volume grows.
•
Data quality & consistency: Applies rules for
cleaning, validating, and standardizing data so
reports are accurate.
•
Tool expertise: Works with platforms like
Snowflake, Amazon Redshift, Google BigQuery,
Azure Synapse, or on-premise solutions like
Teradata and Oracle.
•
Collaboration: Partners with BI developers,
analysts, and data scientists to make sure the
•
warehouse meets business requirements.
Maintenance & governance: Ensures security,
compliance, backup, and monitoring of
warehouse systems.
•
Contact this candidate