Job description

Octus Octus is a leading global provider of credit intelligence, data, and analytics. Since 2013, tens of thousands of professionals across hedge fund, investment banking, management consulting, and law firm verticals have come to rely on Octus to make better, faster, and more confident decisions in pace with the fast-moving credit markets. For more information, visit: octus.com Working at Octus Octus hires growth-minded innovators and trailblazers across the globe to drive our business and culture. Our core values – Action Oriented, Customer First Mindset, Effective Team Players, and Driven to Excel – define an organizational ethos that’s as high-performing as it is human. Among other perks, Octus employees enjoy competitive health benefits, matched 401k and pension plans, PTO, generous parental leave, gym subsidies, educational reimbursements for career development, recognition programs, and much more. Role Octus 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. At Octus, we consider a range of factors in connection with compensation decisions, including experience, skills, location, and our business needs and limitations. As a result, compensation may vary within and across similar roles and positions. Please note that the salary range information below is a good faith estimate for this position and actual compensation for any individual may fall outside this range if warranted by the circumstances applicable to that individual. If we identify a role that would be suitable for a broader range of skills and experience such that we would consider hiring at multiple levels then the range listed below may reflect that breadth. The salary range estimate for this position is $175,000-$245,000 plus an annual bonus. The actual compensation will be at Reorg’s sole discretion and will be determined by the aforementioned and other relevant factors. This position is eligible for an annual year-end performance bonus. Equal Employment Opportunity Octus is committed to providing equal employment opportunities to all employees and applicants for employment without regard to race, colour, religion, sex, sexual orientation, gender identity, national origin, age, disability, genetic information, marital status, pregnancy, veteran status, or any other legally protected status. We strive to create an inclusive and diverse work environment where all individuals are valued, respected, and treated fairly. We believe that diversity enriches our workplace and enhances our ability to innovate and succeed.

Requirements

Must-haves

Working at Octus
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 .
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.

Nice-to-haves

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.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

pythonsqlawsaimlci/cddocker

Platform: greenhouse
Effort: quick

Benefits

Benefits detected in the listing.

401k

Pto

Parental Leave