Lead Data Engineer

Overview

Senior lead designing, building & optimizing enterprise data platforms, migrating to Microsoft Fabric

Key Responsibilities

• fabric migration
• data architecture
• etl pipelines
• performance monitoring
• data governance
• dataops automation

Tasks

-Contributes to the development of goals the area and planning efforts (budgets, operational plans, etc.). -Represent the data engineering team in executive discussions and provide strategic guidance on data initiatives. -Lead the migration from legacy systems to Microsoft Fabric, ensuring seamless integration and minimal disruption. -Partner with business stakeholders, data scientists, and analysts to deliver data solutions that meet business needs. -Recruits, selects, coaches, and develops team members. -Typically, responsible for providing guidance, coaching and training to other employees within job area. -Additional responsibilities for this role:Recruits, selects, coaches, and develops team members.When direct reports are assigned, conducts performance reviews per HR guidelines. Takes corrective actions, including Performance Improvement Plans and terminations, when necessary. Also manages professional growth and development plans. -Maintain clear documentation for data processes, models, and tools. -Monitor and optimize database performance; troubleshoot and resolve complex data issues. -Own the architecture and performance of the enterprise data platform, ensuring reliability and scalability. -Mentor and coach junior data engineers; foster a culture of collaboration and continuous improvement. -Typically, responsible for managing major/complex projects at this level, involving delegation of work and review of work products. -Typically responsible for coaching, reviewing and delegating work to lower-level professionals. -Ensure data quality, security, and compliance with regulatory standards. -Works to achieve day-to-day objectives with major impact on the area of responsibility. -Lead the design and implementation of complex data pipelines, ETL/ELT processes, and integration strategies. -When direct reports are assigned, conducts performance reviews per HR guidelines. Takes corrective actions, including Performance Improvement Plans and terminations, when necessary. Also manages professional growth and development plans. -Provides measurable input to new products, processes or standards in operational plans in order to implement operational objectives. -Provide escalation support for critical production issues, including after-hours when necessary. -Evaluate emerging technologies and recommend solutions that enhance data capabilities and cost efficiency. -Define and implement the organization’s data engineering strategy, aligning with business objectives and future growth. -Manages large projects or processes with moderate impact on the achievement of sub-family results. -Drive adoption of DataOps practices and automation for efficient data delivery.

Requirements

• sql
• python
• pyspark
• databricks
• microsoft fabric
• data vault

What You Bring

-Strong collaboration skills to work across IT and business teams. -Hands-on experience in data warehousing, data lakes, data modeling and ETL/ELT methodologies -Knowledge of DevOps principles and CI/CD for data solutions. -May require travel -Advanced proficiency in SQL for data transformation and performance tuning -Expertise in data governance, security, and compliance frameworks. -Strong understanding of Microsoft Fabric architecture and components: Data Lake, Azure Data Factory, PowerBI -Proven ability to lead technical teams and mentor junior engineers. -Requires deep knowledge of area typically obtained through higher education combined with experience. -Understanding of advanced analytics and AI/ML integration within data platforms. -Typically viewed as a subject matter expert within discipline. May have broad knowledge of project or program management. -Experience with PySpark and Databricks a plus -Typically requires a university degree or equivalent experience and minimum 6-8 years of prior relevant experience. -Experience with Data Vault data modeling a plus -Advanced proficiency in Python for data pipeline development and orchestration -Experience with real-time data processing and big data technologies. -Proficiency in KQL is a plus

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Sector Specialisms