Engineering Team Lead - Data Engineering

Our Team:
Bloomberg runs on data. As the Data Management & Analytics team within Engineering, we support our organization's needs around managing data efficiently and enabling everyone across the company to make informed decisions using self service data analytics tools. We are responsible for ingesting and preparing massive amounts of data and ensuring that the data is properly modeled, governed, accurate and protected for reporting and advanced analytics. A key objective of this role is for you to manage, build and support data analytics programs leveraging data engineering technologies such as Hadoop, Pyspark, Flink, Airflow and traditional MPP Data Warehouse/ETL technologies.

We are looking for a hands-on Team Lead to build and grow our Data Services team. This team will work closely with business partners and engineering teams and ensure seamless integration and processing of complex data sets from various sources onto our analytics platform. If you have a passion towards people management, data and solving unique challenges, we would love to talk to you!

We'll trust you to:

• Manage data services team driving the vision for data analytics across the organization.
• Lead and mentor a group of both senior and junior data engineers, growing them along their career path in order to achieve company and career goals.
• Brainstorm, develop, communicate and promote vision, strategy and roadmap for data analytics and drive innovation across engineering.
• Foster a culture of building high-quality, scalable and robust data engineering applications adhering to strong SDLC practices.
• Cultivate strong relationships with multiple teams ranging from Business, Engineering and other groups within the company.
• Be a hands-on technical leader developing, architecting, standardizing and supporting technology platforms using open source technologies such as Pyspark, Airflow and industry leading ETL solutions.
• Build data pipelines by ingesting petabytes of data using real-time streaming tools like Kafka, Flink.
• Collaborate with other Engineering leaders and integrate data management capabilities such as data modeling, data quality, data anonymization throughout the data lifecycle - data in transit and data at rest.
• Set goals, build OKRs, provide tailored coaching to ensure individual and team performance is on track to meet departmental goals across multiple metric categories such as productivity and quality.
• Stay constantly up to date with industry trends around Data Engineering and bring the latest and greatest innovation and technology stack features from the open source community to our data products including adopting and implementing AI/ML practices.

You'll need to have:

• 3+ years of experience managing and leading data engineering teams.
• 7+ years of experience designing and developing complex data engineering solutions using technologies such as Python/Pyspark, Hadoop/Hive etc.
• Ability to strategically think forward, proactively identify and assess risks and opportunities in a timely manner.
• Experience using real-time streaming frameworks such as Kafka, Flink.
• Strong understanding of data warehousing methodologies, ETL processing and dimensional data modeling.
• Advanced SQL proficiency and data modeling experience. Knowledge of database design techniques and experience working with extremely large data volumes.
• Demonstrated experience working and influencing diverse stakeholders.
• BA, BS, MS, PhD in Computer Science, Engineering or related technology field.

We'd love to see:

• Experience with large databases and Data warehouse implementations.
• A demonstrated commitment to data governance, security, and compliance, ensuring the integrity and privacy of the data under your management.
• Experience in data anonymization, data quality, and data lineage.
• Experience with orchestration tools such as Airflow.
• Knowledge of reporting tools such as QlikSense, Tableau, PowerBI.