The Friedkin Group - Tomball, TX

posted 2 months ago

Full-time - Senior
Tomball, TX
Securities, Commodity Contracts, and Other Financial Investments and Related Activities

About the position

As a Lead Data Engineer within the Trailblazer initiative at The Friedkin Group, you will play a crucial role in architecting, implementing, and managing robust, scalable data infrastructure. This position demands a blend of systems engineering, data integration, and data analytics skills to enhance TFG's data capabilities, supporting advanced analytics, machine learning projects, and real-time data processing needs. You will be responsible for designing and implementing scalable and reliable data pipelines to ingest, process, and store diverse data at scale, utilizing technologies such as Apache Spark, Hadoop, and Kafka. In this role, you will work within cloud environments like AWS or Azure, leveraging services including but not limited to EC2, RDS, S3, Lambda, and Azure Data Lake for efficient data handling and processing. You will develop and optimize data models and storage solutions (SQL, NoSQL, Data Lakes) to support operational and analytical applications, ensuring data quality and accessibility. Additionally, you will utilize ETL tools and frameworks (e.g., Apache Airflow, Talend) to automate data workflows, ensuring efficient data integration and timely availability of data for analytics. Collaboration is key in this position, as you will work closely with data scientists, providing the necessary data infrastructure and tools for complex analytical models, leveraging Python or R for data processing scripts. You will also ensure compliance with data governance and security policies, implementing best practices in data encryption, masking, and access controls within a cloud environment. Monitoring and troubleshooting data pipelines and databases for performance issues will be part of your responsibilities, applying tuning techniques to optimize data access and throughput. Staying abreast of emerging technologies and methodologies in data engineering will be essential, as you advocate for and implement improvements to the data ecosystem.

Responsibilities

  • Design and implement scalable and reliable data pipelines to ingest, process, and store diverse data at scale, using technologies such as Apache Spark, Hadoop, and Kafka.
  • Work within cloud environments like AWS or Azure to leverage services including EC2, RDS, S3, Lambda, and Azure Data Lake for efficient data handling and processing.
  • Develop and optimize data models and storage solutions (SQL, NoSQL, Data Lakes) to support operational and analytical applications, ensuring data quality and accessibility.
  • Utilize ETL tools and frameworks (e.g., Apache Airflow, Talend) to automate data workflows, ensuring efficient data integration and timely availability of data for analytics.
  • Collaborate closely with data scientists, providing the data infrastructure and tools needed for complex analytical models, leveraging Python or R for data processing scripts.
  • Ensure compliance with data governance and security policies, implementing best practices in data encryption, masking, and access controls within a cloud environment.
  • Monitor and troubleshoot data pipelines and databases for performance issues, applying tuning techniques to optimize data access and throughput.
  • Stay abreast of emerging technologies and methodologies in data engineering, advocating for and implementing improvements to the data ecosystem.

Requirements

  • Bachelor's Degree in computer science, MIS, or other business discipline and 10+ years of experience in data engineering, with a proven track record in designing and operating large-scale data pipelines and architectures.
  • Master's Degree in computer science, MIS, or other business discipline and 5+ years of experience in data engineering, with a proven track record in designing and operating large-scale data pipelines and architectures.
  • Expertise in developing ETL/ELT workflows.
  • Comprehensive knowledge of platforms and services like Databricks, Dataiku, and AWS native data offerings.
  • Solid experience with big data technologies (Apache Spark, Hadoop, Kafka) and cloud services (AWS, Azure) related to data processing and storage.
  • Strong experience in AWS and Azure cloud services, with hands-on experience in integrating cloud storage and compute services with Databricks.
  • Proficient in SQL and programming languages relevant to data engineering (Python, Java, Scala).
  • Hands-on RDBMS experience (data modeling, analysis, programming, stored procedures).
  • Familiarity with machine learning model deployment and management practices is a plus.
  • Strong communication skills, capable of collaborating effectively across technical and non-technical teams.

Nice-to-haves

  • AWS Certified Solution Architect Preferred
  • Databricks Certified Associate Developer for Apache Spark Preferred
  • Azure Data Engineer Associate Preferred
  • Other relevant certifications.

Benefits

  • Medical insurance
  • Dental insurance
  • Vision insurance
  • Wellness programs
  • Retirement plans
  • Generous paid leave
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