Senior Data Engineering Manager

Key Skills

Algorithms, Amazon RedShift, Amazon Simple Storage Service (S3), Analytical and Problem solving, Apache Hadoop, Apache Hive, AWS, Business Intelligence, Cloud computing, Crypto, CSS, Data Analytics, Data Biuld Tool - dbt, Data Modeling, Data pipelines, Data structuring, Data Warehousing, Databricks, Design, Effective communication skills, ETL frameworks, Finance, Google BigQuery, HDFS, HTML, JavaScript, Looker, Machine learning techniques, Natural Language Processing (NLP), Python Programming, Snowflake, Software Development, Spark Core, SQL, Tableau

Job Description

Gemini customers also have access to a wide range of institutional products tailor-made for high-net-worth individuals, asset and wealth managers, and hedge funds and liquidity providers seeking exposure to crypto. Customers looking to place large orders can use Gemini eOTC, a fully-electronic over-the-counter trading platform built for high-value bulk orders. For wealth management professionals, we offer a unique destination for their clients’ crypto portfolios from a single platform, and we enable fully electronic clearing and settlement of off-exchange crypto trades.

The Department: Analytics

Data and analytics are central to all of our business functions and drive many of our most important decisions at Gemini. The Analytics team is responsible for data architecture, data engineering, business intelligence, machine learning, and data governance functions that shape the way data is stored and leveraged across Gemini. Data engineers and Machine Learning engineers make up the Analytics team are responsible for building the primary decision support system that derives continuous value by enabling individuals and various functional groups to make data driven informed decisions via our reliable data processes, data products and advanced analytics ability. The projects executed by the team cover a wide-range of topics including user acquisition and customer journey, cryptocurrency performance, product analytics, order book analytics, risk analytics, enabling automated and scalable blockchain based reconciliation systems, building predictive models all the way to enabling anomaly and fraud detection.

The Role: Senior Manager, Data Engineering

As a Senior Manager in Data Engineering at Gemini, you will play a pivotal role in guiding your team to define roadmaps, plans, and goals, while providing support to help them achieve these objectives. As a leader of our data engineering team, you will set standards for impactful data engineering solutions. You will provide architectural solutions that are efficient, robust, extensible, and competitive within the business and industry context. Leveraging your deep involvement and experience in building cloud systems at scale, you will lead the team in constructing robust, scalable, and highly available systems. Moreover, you will collaborate with senior data engineers, machine learning engineers, and analysts, guiding them towards their career goals at Gemini. Effective communication of your insights with leaders across the organization will be paramount to your success in this role.

Responsibilities:

• Design, architect, and implement best-in-class Data Warehousing and reporting solutions, build real-time data and reporting solutions, and develop new systems and tools to enhance data consumption and understanding for the teams.
• Provide strong management and leadership to multiple data engineering teams, setting goals, fostering a high-performance culture, and driving performance.
• Lead talent acquisition and recruitment efforts, actively participating in candidate evaluations and interviews.
• Promote knowledge sharing, create maintainable software, and provide technical input to enhance product planning, design, and security review processes.
• Foster a diverse, equitable, and inclusive environment, provide feedback and coaching to team members, and communicate effectively at all levels.
• Collaborate with cross-functional teams to align data engineering efforts with organizational goals.
• Effectively resolve ambiguity, analyze complex requirements, break down features and initiatives into tasks, and take ownership of the team's output and performance.

Minimum Qualifications:

• 10+ years of combined software development and management experience in data engineering, working with multiple languages and frameworks, or equivalent experience.
• 3+ years of experience managing data engineering teams and delivering high-quality software for customers.
• Proven track record of establishing successful partnerships with key stakeholders, including product managers, project managers, and business owners.
• Extensive experience in data engineering, covering data warehouse technologies, custom ETL design, implementation, maintenance, and schema design with dimensional data modeling and orchestration.
• Proficiency in data lake design and implementation, showcasing a strong understanding of principles and best practices.
• Strong expertise in building real-time data solutions and processes, with advanced skills in Python and SQL.
• Experience and proficiency in Databricks, Spark, Hadoop, and MPP databases (such as Redshift, BigQuery, Snowflake).