M Science is a data-driven research and analytics firm, uncovering new insights for leading financial institutions and corporations. We come to work every day to create actionable insights from alternative data. M Science is revolutionizing research, discovering new data sets and pioneering methodologies to provide actionable intelligence. Our research teams have decades of experience working with massive amounts of unstructured data in near real-time to discern critical insights that help clients make smarter, more informed decisions. We combine the best of finance, data and technology to create a truly unique value proposition for both financial services firms and major corporations.

We’re looking for exceptional data engineers who will work with petabytes of data daily using diverse technologies like Databricks, SQL, Python, Airflow, and others. You will be expected to effectively partner with upstream engineering teams and downstream consumers, including analysts and product engineers. In this role, you will build datasets to support analytics, experimentation, and machine learning. Specifically, you will build out standards for pipelining at scale, including establishing common operators, workflows/DAGs, and optimizing existing products.

NOTE: This role requires flexible working hours to ensure at least 2 hours of overlap with colleagues based on both the US East Coast and West Coast. Expected working schedule: 12:00–20:00 CET.

Responsibilities:

• Manage and improve pipeline for cleansing, filtering, and structuring raw datasets for the purposes of company and customer analysis.
• Implement QA procedures to proactively identify data anomalies and issues.
• Implement new products and features created by analyst teams using best practices.
• Work with other team members and internal stakeholders on infrastructure projects, documentation, QA and code reviews.
• Build and manage Repos.
• Continuously seek opportunities to standardize code and automate production pipelines.
• Create and contribute to frameworks that improve the efficacy of logging data, while working with infrastructure to triage issues and resolve.
• Mentor team members by giving/receiving actionable feedback.

Requirements:

• 3-7 years of experience as a data engineer.
• Preference given to those that have worked in a similar type role within a data intelligence company or within our desired verticals including consumer retail & brands, digital media and technology.
• Expertise in data engineering in a cloud environment (Python, Databricks, AWS, Airflow, Snowflake, SQL).
• SQL & Python required.
• Working knowledge of PySpark and distributed computing.
• Strong people and management skills to interact with product managers and data analysts.
• Familiarity with software development methods such as Scrum and TDD.
• General project management skills.
• Ability to clearly articulate project plans, technical requirements, methodologies, and takeaways to both technical and non-technical stakeholders.
• Bachelor’s or higher degree, or significant experience in Statistics, Mathematics, Computer Science, or a similar quantitative discipline.
• Ability to thrive in a culture of quality and personal accountability.

What we offer:

• 20 days of vacation.
• Public holidays.
• Mobilization reservation available upon successful completion of the probation period (for Ukrainian colleagues).
• The exchange of experience and work with talented colleagues.
• Last but not least - valuable compensation for your efforts.