RoleOctus is seeking a
Principal Data Engineer to design, develop, and lead scalable data ingestion and transformation pipelines. This is a
hands-on Individual Contributor (IC) role that encompasses some management duties, including mentoring, architecture guidance, and providing technical leadership. You'll play a key role in designing and maintaining robust data infrastructure that powers data platforms, products and automation initiatives across the firm. The ideal candidate is an expert Python and SQL developer with deep experience building modern data workflows using AWS services and infrastructure-as-code.
Responsibilities- Lead the overarching technical strategy for the data platform, ensuring alignment between data infrastructure and long-term business goals.
- Lead the design and development of data ingestion and transformation pipelines, ensuring scalability, efficiency, and reliability across diverse data sources (APIs, web data, internal feeds, etc.).
- Serve as a foundational technical leader and mentor for senior engineers within the data platform team, guiding architecture, design, and implementation decisions.
- Architect and manage data pipelines and orchestration workflows using AWS services such as MWAA (Airflow), Lambda, ECS, and SQS.
- Implement and maintain infrastructure as code (IaC) using Terraform, ensuring reproducibility and compliance with cloud standards.
- Partner with data analysts, scientists, and backend engineers to ensure data consistency, discoverability, and reliability.
- Apply best practices in data modeling, schema design, and ETL/ELT processes for high-volume structured and semi-structured data.
- Ensure data quality and lineage through automated testing, monitoring, and alerting.
- Promote continuous improvement through code reviews, observability practices, and team-wide knowledge sharing.
- Collaborate closely with technology leadership to align data platform development with business strategy and product goals.
- Stay up to date with industry trends in data engineering, cloud architecture, AI/ML integration, and automation.
Requirements- Strong foundation in software engineering principles, including SOLID design, modularity, and scalability.
- Expert proficiency in Python for data pipeline and automation development.
- Advanced SQL skills and experience optimizing complex queries and data models.
- Proven experience designing and maintaining cloud-native data pipelines on AWS (e.g., MWAA/Airflow, Lambda, ECS, SQS, Glue, S3, Redshift, etc.).
- Experience with data warehousing or lakehouse technologies (Redshift, Snowflake, Databricks, etc.).
- Experience implementing and managing Terraform or similar IaC frameworks.
- Strong understanding of data ingestion, transformation, and orchestration tools and patterns, including those used in AI/ML pipelines.
- Familiarity with CI/CD pipelines, automated testing, and modern DevOps practices.
- 8+ years of experience in data engineering or backend development, with a focus on scalable data solutions.
- Extensive experience in a technical leadership capacity, including mentoring and leading complex data infrastructure projects end-to-end.
- Familiarity with containerization (Docker) and workflow orchestration best practices.
- Excellent communication, collaboration, and problem-solving skills.
Nice to Have- Exposure to streaming data technologies (Kafka, Kinesis, Flink).
- Experience integrating data quality and observability tools (Great Expectations, Monte Carlo, etc.).
- Familiarity with Scrapy, BeautifulSoup, or other data extraction frameworks for ingestion pipelines.
