Data Engineering Solutions Lead (Remote, based in Houston or Austin TX)

The Friedkin Group is a prominent organization seeking a Data Engineering Solutions Lead to enhance their data capabilities through robust data infrastructure. The role involves designing scalable data pipelines, optimizing data models, and leading the Data Management Community of Practice to support advanced analytics and machine learning projects.

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 but not limited to EC2, RDS, S3, Lambda, and Azure Data Lake for efficient data handling and processing
• Architect and operationalize data pipelines following Medallion Architecture best practices within a Lakehouse framework—ensuring data quality, lineage, and usability across Bronze, Silver, and Gold layers
• Develop and optimize data models and storage solutions (Databricks, Data Lakehouses) to support operational and analytical applications, ensuring data quality and accessibility
• Utilize ETL tools and frameworks (e.g., Apache Airflow, Fivetran) to automate data workflows, ensuring efficient data integration and timely availability of data for analytics
• Lead the Data Management Community of Practice, serving as the primary facilitator, coordinator, and spokesperson. Drive knowledge sharing, establish best practices, and represent the data engineering discipline across TFG to both technical and business audiences
• 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 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
• Demonstrated experience designing and implementing Medallion Architecture in a Databricks Lakehouse environment, including layer transitions, data quality enforcement, and optimization strategies
• 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 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 executive presence and communication skills, with a proven ability to lead communities of practice, deliver presentations to senior leadership, and build alignment across technical and business stakeholders
• AWS Certified Solution Architect
• Databricks Certified Associate Developer for Apache Spark
• DAMA CDMP or other relevant certifications