Data & Analytics Lead

Key Responsibilities

• Oversee the development and deployment of enterprise-level analytical data products, including potential pilots and proof of concepts, while determining suitable design strategies and methodologies.
• Innovate solutions to complex challenges that may have broad implications, reflecting on outcomes, measuring effects, and leveraging that data for ideation and optimization.
• Implement data strategies with a comprehensive understanding of enterprise architecture, consumption patterns, platforms, and application infrastructure.
• Forge business partnerships and influence priorities by identifying solutions that align with current business goals and adhere to industry trends.
• Engage with partners to explain technological concepts in accessible terms, document initiatives clearly, and actively listen to others’ perspectives and ideas.
• Collaborate with data management and information security teams to safeguard highly sensitive datasets.
• Sustain relationships with partner teams to ensure their needs are met and that impacted areas plan their work effectively.
• Deliver analyses and recommendations that influence strategic decision-making.
• Take the lead to inspire and motivate others, effectively influencing team members.
• Support and mentor team members, emphasizing individual professional growth alongside overall team health and technical skills.

Qualifications of the Ideal Candidate

• At least 10 years of experience in building, managing, and leading high-performing, diverse, collaborative, and geographically distributed data engineering teams.
• Proven capability as a strategic technical partner, collaborating effectively with data analytics, data science, product, engineering, and other cross-functional teams to plan, prioritize, and meet company objectives.
• Experience in initiating, leading, and evolving technical forums, along with strong interpersonal skills, while representing data engineering and architectural considerations across teams.
• Track record of developing and mentoring data engineering talent, including the implementation of training, upskilling, and mentorship programs. Expert knowledge in engineering, architecture, and system design, supported by strong computer science fundamentals.
• Proficiency in developing efficient and scalable production software using Python, Java, or other programming languages prevalent in data engineering.
• Familiarity with event-driven and/or streaming workflows utilizing tools such as Kafka and Spark, alongside expertise in ETL concepts and tools, including experience with ingesting, processing, and transforming diverse data at scale. Proficient in SQL and NoSQL databases, data warehousing principles, and cloud-based analytics databases.
• Experience with various tools and platforms (or similar), including Azure (ADLS, ADF, AKS, Synapse, Purview), Databricks, Python, JavaScript, Kafka, dbt, Terraform, Snowflake, SQL, Jenkins, Github, Airflow, and MLFlow. Knowledge of concepts such as Real-time & Batch Data Processing, Workload Orchestration, Cloud, Datalakes, Data Security, Networking, Serverless architecture, Testing/Test Automation (Unit, Integration, Performance, etc.), Web Services, DevOps, Logging, Monitoring, Alerting, Containerization, Encryption/Decryption, Data Masking, and Cost & Performance Optimization.
• Exceptional written and verbal communication skills, with a demonstrated ability to convey complex concepts to diverse audiences.
• Capacity to excel in a fast-paced, agile, and dynamic environment while maintaining a positive attitude. Ability to manage multiple concurrent projects independently and collaboratively with teams of varying sizes and technical backgrounds.
• Bachelor's or Master's degree in Computer Science or a related field; team leadership and management training is advantageous.