What are the responsibilities and job description for the Sr. Lead Data Engineer - ONLY W2 position at ExecutivePlacements.com?
Job Title: Sr. Lead Data Engineer (Only W2)
Duration: 9 Months Contract with possibility to extension
LOCATION: ** PREFERRED: Home office in Huntsville, TX. May work remotely, but would need the capability to report to the office with advanced notice. **
Job Description
We are seeking a highly skilled and experienced professional to lead the design, implementation, and management of end-to-end enterprise-grade data solutions. This role involves expertise in building and optimizing data warehouses, data lakes, and lakehouse 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.
DISCLAIMER: The information transmitted is intended only for the person or entity to which it is addressed and may contain confidential and/or privileged material. Any review, retransmission, dissemination or other use of, or taking of any action in reliance upon, this information by persons or entities other than the intended recipient is prohibited. If you received this in error, please contact the sender and delete the material from any computer or if you want to be REMOVED please reply with REMOVE in the Subject line of this email.
Duration: 9 Months Contract with possibility to extension
LOCATION: ** PREFERRED: Home office in Huntsville, TX. May work remotely, but would need the capability to report to the office with advanced notice. **
Job Description
We are seeking a highly skilled and experienced professional to lead the design, implementation, and management of end-to-end enterprise-grade data solutions. This role involves expertise in building and optimizing data warehouses, data lakes, and lakehouse 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.
- Education: Bachelor's Computer Science, Data Science, Engineering, or a related field.
- Experience: Minimum 10 years in data engineering, data architecture, or a similar role, with at least 3 years in a lead capacity.
- 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.
- 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.
- 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.
DISCLAIMER: The information transmitted is intended only for the person or entity to which it is addressed and may contain confidential and/or privileged material. Any review, retransmission, dissemination or other use of, or taking of any action in reliance upon, this information by persons or entities other than the intended recipient is prohibited. If you received this in error, please contact the sender and delete the material from any computer or if you want to be REMOVED please reply with REMOVE in the Subject line of this email.
