Manager, Data Engineering

About the role

As the Manager, Data Engineering, you will set goals and objectives for the team responsible for designing, building, and maintaining robust data systems that enable data analysis and reporting. You will lead the end-to-end process to ensure large sets of data are efficiently processed and made accessible for decision-making.

Responsibilities

• Data & Analytical Solutions: Oversee the development of data products and solutions using big data and cloud-based technologies, ensuring they are scalable, sustainable, and robust.
• Data Pipelines: Develop and monitor streaming and batch data pipelines that facilitate seamless ingestion of data from various sources, transform it into actionable information, and move it to data stores like data lakes and warehouses.
• Data Systems: Review existing data systems and architectures to identify areas for improvement and optimization.
• Data Infrastructure: Oversee the preparation of data infrastructure to drive efficient storage and retrieval of data.
• Data Formats: Review and resolve appropriate data formats to improve usability and accessibility across the organization.
• Stakeholder Management: Partner collaboratively with multi-functional data and advanced analytics teams to capture requirements and ensure data solutions meet functional and non-functional needs.
• Data Frameworks: Build complex prototypes to test new concepts and provide guidance on implementing data engineering frameworks and architectures that improve data processing and support advanced analytics.
• Automated Deployment Pipelines: Oversee the development of automated deployment pipelines to improve efficiency with fit-for-purpose governance.
• Data Modeling: Guide the team in performing data modeling aligned with datastore technology to ensure sustainable performance and accessibility.
• Team Management: Manage team members to achieve organizational goals by ensuring productivity, communicating performance expectations, providing coaching, measuring progress, and fostering an inclusive team culture.

Qualifications

Minimum Requirements:

• 4 years of relevant work experience (typically reflects 5+ years of experience).

Preferred Qualifications:

• Data Engineering: Experience with data engineering on corporate finance data.
• Cloud Environments: Familiarity with major cloud platforms (AWS, GCP, Azure).
• Data Architecture: Experience with modern data architectures, including data lakes, data lakehouses, and data hubs, along with related capabilities such as ingestion, governance, modeling, and observability.
• Data Ingestion: Proficiency in data collection, ingestion tools (Kafka, AWS Glue), and storage formats (Iceberg, Parquet).
• Data Streaming: Knowledge of streaming architectures and tools (Kafka, Flink).
• Data Modeling: Strong background in data transformation and modeling using SQL-based frameworks and orchestration tools (dbt, AWS Glue, Airflow). Experience with modeling concepts like SCD and schema evolution.
• Data Transformation: Familiarity with using Spark for data transformation, including streaming, performance tuning, and debugging with Spark UI.
• Programming: Proficient in Python, Java, Scala, or similar languages. Expert-level proficiency in SQL for data manipulation and optimization.
• DevOps: Demonstrated experience in DevOps practices, including code management, CI/CD, and deployment strategies.
• Data Governance: Understanding of data governance principles, including data quality, privacy, and security considerations for data product development and consumption.

The business will not sponsor work visas for this position.