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Lead Data Engineer

2 weeks ago

Houston, Texas, United States eStaff LLC Full time $120,000 - $200,000 per year

We are seeking a W2 (We cannot work with C2Cs) Lead Data Engineer for a hybrid position (Must commute to Huntsville, Texas when required
). Will lead the design, implementation, and management of end-to-end, enterprise-grade data solutions for our Huntsville, Texas client. This role requires expertise in building and optimizing data warehouses, data lakes, and lake house platforms, with a strong emphasis on data engineering, data science, and machine learning. You will work closely with cross-functional teams to create scalable and robust architectures that support advanced analytics and machine learning use cases, while adhering to industry standards and best practices.

Responsibilities Include:


• Architect, design, and manage the entire data lifecycle from data ingestion,

transformation, storage, and processing to advanced analytics and machine learning databases and large-scale processing systems.


• Implement robust data governance frameworks, including metadata management, lineage tracking, security, compliance, and business glossary development.


• Identify, design, and implement internal process improvements, including redesigning infrastructure for greater scalability, optimizing data delivery, and automating manual

processes.


• Ensure high data quality and reliability through automated data validation and testing, and provide high-quality, clean, and usable data from data sets of varying states of disorder.


• Develop and enforce architecture standards, patterns, and reference models for large-scale data platforms.


• Architect and implement Lambda and Kappa architectures for real-time and batch data processing workflows, along with strong data modeling capabilities.


• Ability to identify and implement the most appropriate data management system and enable integration capabilities for external tools to perform ingestion, compilation, analytics and visualization.

Required:

* Bachelor's in Computer Science, Data Science, Engineering, or a related field.

* Minimum 10 years in data engineering, data architecture, or a similar role, with at least 3 years in a lead capacity.

* Proficient in SQL, Python, and big data processing frameworks (e.g., Spark, Flink).

* Strong experience with cloud platforms (AWS, Azure, GCP) and related data services.

* Hands-on experience with data warehousing tools (e.g., Snowflake, Redshift, BigQuery), Databricks running on multiple cloud platforms (AWS, Azure, and GCP), and data lake technologies (e.g., S3, ADLS, HDFS).

* Expertise in containerization and orchestration tools like Docker and Kubernetes.

* Knowledge of MLOps frameworks and tools (e.g., MLflow, Kubeflow, Airflow).

* Experience with real-time streaming architectures (e.g., Kafka, Kinesis).

* Familiarity with Lambda and Kappa architectures for data processing.

* Enable integration capabilities for external tools to perform ingestion, compilation, analytics, and visualization.

Preferred:

* Certifications in cloud platforms or data-related technologies.

* Familiarity with graph databases, NoSQL, or time-series databases.

* Knowledge of data privacy regulations (e.g., GDPR, CCPA) and compliance requirements.

* Experience in implementing and managing business glossaries, data governance rules, metadata lineage, and ensuring data quality.

* Highly experienced with AWS cloud platform and Databricks Lakehouse.