Data Engineer Lead

Tidal Financial Group

75d

About The Position

We are seeking a Lead Data Engineer to architect, scale, and optimize the data ecosystem that powers Tidal’s ETF platform. This role sits at the intersection of data engineering, analytics, and infrastructure—responsible for designing and leading robust, cloud-native data pipelines and ensuring high-performance data delivery across business and operational systems. You will lead technical design, guide data engineering best practices, and collaborate with cross-functional partners to ensure that our data infrastructure supports accurate, timely, and compliant decision-making across the organization.

Requirements

Bachelor’s or Master’s degree in Computer Science, Data Engineering, or related experience in a technical field.
7+ years of professional experience in data engineering, including leadership or senior-level design responsibilities.
Experience building and scaling cloud-based data architectures in financial or fintech environments.
Expert in Python and SQL for data transformation, pipeline orchestration, and automation.
Deep knowledge of data warehousing (Snowflake, Redshift, or equivalent) and cloud platforms (AWS preferred).
Experience with modern orchestration and transformation tools (Airflow, dbt, Prefect).
Familiarity with APIs, event-driven pipelines (Kafka or Kinesis), and ETL frameworks.
Strong grasp of version control (Git/GitHub), CI/CD, and Agile methodologies.
Familiarity with financial datasets (ETFs, trading, market data) and API integrations (Bloomberg, custodians, fund admins).
Excellent communication and collaboration skills with technical and non-technical teams.
Strategic mindset with the ability to balance long-term architecture vision and short-term delivery needs.
Proven ability to mentor others, foster team learning, and build a culture of engineering excellence.
Detail-oriented, proactive, and deeply committed to data reliability and quality.

Responsibilities

Data Architecture & Pipeline Leadership
Design, build, and optimize scalable data pipelines and architectures in AWS (or equivalent), leveraging technologies such as Snowflake, dbt, and modern orchestration frameworks (Airflow, Prefect, etc.).
Integrate complex financial data sources—Bloomberg APIs, custodial feeds, and fund administration data—into reliable, auditable data flows.
Develop and maintain a modular, well-documented data ecosystem supporting analytics, reporting, and operations at scale.
Lead reviews of architecture and ETL design patterns, ensuring performance, cost efficiency, and maintainability.
Data Quality & Observability
Implement automated data validation and testing frameworks (e.g., Great Expectations or similar).
Establish proactive monitoring and alerting for data pipeline performance, reliability, and accuracy.
Define and enforce standards for data lineage, versioning, and documentation within the engineering environment.
Partner with governance and compliance teams to ensure that all financial data meets Tidal’s quality and regulatory expectations.
Collaboration & Business Enablement
Partner with sales, operations, and trading teams to define data requirements and ensure the infrastructure delivers actionable insights.
Collaborate with analytics teams to optimize models, datasets, and query performance in BI tools such as Sigma.
Support automation and data-driven decision-making across the ETF product lifecycle.
Technical Leadership & Mentorship
Provide hands-on technical guidance to data engineers and analysts, promoting best practices in coding, data modeling, and system design.
Contribute to and maintain a robust data engineering playbook covering design standards, CI/CD, and deployment practices.
Lead architectural planning, sprint reviews, and code reviews to uphold engineering excellence and operational reliability.
Stay current on emerging technologies and patterns in data engineering, cloud architecture, and financial systems integration.