The Data Engineer will be responsible for:
Data Strategy and Governance
- Partner with data management and business stakeholders to design and implement scalable data solutions that align with enterprise data strategy and governance frameworks.
- Contribute to defining and enforcing data standards, ensuring consistency, traceability, and adherence to architectural principles across ingestion, transformation, and consumption layers.
- Collaborate with data stewards and governance teams to promote data quality, lineage visibility, and stewardship processes within Azure and Snowflake ecosystems.
- Support efforts to transition ad hoc, manual data processes into governed, automated, and well-documented pipelines that improve transparency, auditability, and reusability
- Ensure compliance with security, privacy, and regulatory requirements across all aspects of data storage, movement, and access.
Data Engineering and Operations
- Create and manage data ingestion pipelines, setting up and maintaining data feeds and supporting both batch and near real-time integrations across internal and external systems to integrate them into the firm's environment securely and efficiently, in collaboration with the firm’s software engineers and internal applications.
- Apply strong SQL engineering skills to optimize performance across Snowflake, Azure, and PostgreSQL, leveraging stored procedures, views, ingest and transformation tools and performance tuning techniques.
- Implement transformation logic and data workflows, adhering to version control and continuous integration/continuous delivery (CI/CD) pipelines.
- Establish observability and monitoring frameworks to ensure data reliability, pipeline health, and SLA compliance.
- Collaborate with data architects to align solutions with enterprise data models, ensuring scalability, maintainability, and cost efficiency for both OLTP and OLAP paradigms.
- Contribute to tool evaluation and deployment, architecture reviews, and process automation to continually enhance data platform performance and maturity.
Reporting and Analytics
- Partner with analytics and BI teams to ensure availability and reliability of curated, high-quality data sets that power reporting and machine learning initiatives.
- Build and optimize semantic layers and curated datasets for Power BI and other visualization tools, supporting self-service analytics and governed data access.
- Contribute to data cataloging, lineage documentation, and metadata management initiatives to enhance discoverability and reusability across the enterprise.
- Collaborate closely with data consumers to understand analytical needs, ensuring that data models and transformations align with business requirements and performance expectations.
Innovation and Continuous Improvement
- Evaluate and introduce new technologies, frameworks, and methodologies that improve data processing efficiency, quality, and scalability.
- Lead or contribute to POCs and platform modernization efforts, helping evolve toward cloud-native and metadata-driven architectures.
- Continuously improve CI/CD processes, data testing, and documentation standards to promote reliability and repeatability.
- Champion best practices in data engineering, including modular design, reusable components, and performance optimization.
- Stay current with emerging trends in cloud data architecture, data mesh, and automation tools to ensure the organization remains innovative and competitive.
SKILLS DESIRED
Qualifications & Experience
- Education: Bachelor’s degree (BA/BS) in Data Engineering, Computer Science, Information Systems, or a related field. Master’s degree a plus.
- 4-7 years of experience in roles related to data engineering or related roles.
- Strong proficiency in SQL with Azure, PostgreSQL and Snowflake
- Strong proficiency in Python, Java, and other programming languages.
- Hands-on experience with Azure Data Factory, Apache Airflow, Mulesoft, and Apache Airflow for orchestration and scheduling
- Strong knowledge of data integration and ETL/ELT processes, and transformation tools, such as DBT or Coalesce.
- Experience implementing CI/CD pipelines for data infrastructure and deployments (e.g., using Azure DevOps or GitHub Actions).
- Demonstrated knowledge of data modeling, metadata management, and data lineage tools.
- Familiarity with data governance, quality frameworks, and compliance standards.
- Experience in financial services or legal industries is preferred but not required.
Pay: $125,000.00 - $140,000.00 per year
Work Location: Remote
