Data Specialist at Dime Line Trading

Chicago, Illinois, United States -

Full Time

Start Date

Immediate

Expiry Date

07 Jun, 26

Salary

0.0

Posted On

09 Mar, 26

Experience

2 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

SQL, Python, Data Pipeline Maintenance, Data Organization, Query Optimization, Data Retrieval, Data Transformation, Data Cleaning, Data Normalization, Orchestration Jobs, Airflow, Communication, Data Modeling, Schema Design, AI Tools

Industry

Financial Services

Description

We're looking for a detail-oriented, SQL- and Python-adept analyst who will serve as the connective tissue between our data and the people who use it. This isn't a software engineering role — it's for someone who genuinely loves knowing what data lives where, why it's structured the way it is, the processes used to generate and store it, and how to help others get the most out of it. What you'll do: You'll be the go-to resource for quants who need help pulling data — showing them where to look, constructing sample queries and notebooks to guide their exploration, and building out generalized tooling for data retrieval. You'll own documentation and organization of our datasets: what tables exist, what they contain, how they relate, and what conventions make sense given how the data gets used downstream. You'll also own the maintenance of our data pipelines — writing and updating the code that moves and transforms data, handling the cleaning and normalization work that comes with ingesting data from varied sources, and keeping things running reliably over time. When improvements to storage or access need to be deployed, you'll work closely with our DevOps team to make that happen. What we're looking for: A systematic, organized mind — you think in primary keys, data types, and relationships Comfort mapping and documenting data landscapes across multiple systems Relative SQL fluency, including a real understanding of query efficiency and optimization Proficiency in Python (or similar) for data pipeline work — cleaning, transforming, and moving data Enough technical range to set up and maintain orchestration jobs (e.g., Airflow) Strong communication skills; you'll regularly translate between technical and non-technical stakeholders Predictable and reliable availability Nice to have: Experience in a data-intensive industry (finance, sports, ad tech, etc.) Familiarity with columnar or analytical databases Background in data modeling or schema design Ability to use AI tools to improve the efficiency of querying internal data

Responsibilities

The specialist will serve as the primary resource for data users, assisting with data retrieval, constructing sample queries, and building generalized tooling for data access. Responsibilities also include owning dataset documentation, organization, and maintaining data pipelines through writing and updating transformation code.