Data Engineer

Solar Digital Agro LLC; Chicago (IL); On-site; USD 110,000 - 160,000; Full time

Job summary

Solar Digital Agro LLC is seeking a Financial Data Engineer to build and maintain data infrastructure powering trading, quantitative research, risk management, and reporting. You will handle market data, orders, executions, positions, P&L, and other financial datasets.

The role welcomes junior to senior candidates and emphasizes production-grade pipelines, data reliability, and collaboration with traders, researchers, and engineers. Location in Chicago area; visa sponsorship not specified.

Qualifications

• Strong Python and SQL skills and experience with relational databases.
• Experience designing ETL/ELT pipelines and production data workflows.
• Familiarity with APIs, streaming data, and large datasets.

Responsibilities

• Build and maintain data pipelines for market data, orders, executions, positions, cash balances, P&L, and risk records.
• Collect and integrate data from broker APIs, market-data providers, exchanges, databases, and internal systems.
• Process both real-time streaming data and historical batch data.
• Design reliable data models for financial instruments, accounts, portfolios, and transactions.
• Store and manage tick, intraday, daily, and reference data.
• Build data-quality checks for missing records, duplicate data, incorrect timestamps, price anomalies, and reconciliation differences.
• Develop automated reconciliation across orders, executions, positions, cash balances, and broker statements.
• Support quantitative researchers with clean, consistent, point-in-time datasets for research and backtesting.
• Support risk teams with account-level and portfolio-level monitoring data.
• Develop internal data services, APIs, dashboards, alerts, and automated reports.
• Improve database performance, system scalability, monitoring, and fault recovery.
• Maintain clear documentation, data definitions, lineage, and operational procedures.
• Collaborate with traders, quantitative researchers, risk managers, and software engineers

Skills

Python

SQL

Relational databases

ETL/ELT pipelines

APIs / streaming data

Linux / Git / testing / logging

Data accuracy & reliability

Data discrepancies investigation

English / Mandarin communication

Tools

PostgreSQL / TimescaleDB / ClickHouse

Kafka / Redpanda / Redis / Airflow / Dagster

Pandas / Polars / PySpark

IBKR API / broker APIs

Real-time streaming

DB optimization / partitioning

Data monitoring / alerting / reconciliation

Job description

We are looking for a Financial Data Engineer to help build and maintain the data infrastructure supporting our trading, quantitative research, risk management, and operational reporting.

This position focuses on financial data engineering rather than general business analytics. You will work with market data, orders, executions, positions, P&L, risk metrics, account activity, and other financial datasets.

We welcome candidates at different career stages. Junior candidates should demonstrate strong technical fundamentals and relevant projects, while experienced candidates should be prepared to take ownership of system architecture, data reliability, and production workflows.

What You’ll Work On

• Build and maintain data pipelines for market data, orders, executions, positions, cash balances, P&L, and risk records
• Collect and integrate data from broker APIs, market-data providers, exchanges, databases, and internal systems
• Process both real-time streaming data and historical batch data
• Design reliable data models for financial instruments, accounts, portfolios, and transactions
• Store and manage tick, intraday, daily, and reference data
• Build data-quality checks for missing records, duplicate data, incorrect timestamps, price anomalies, and reconciliation differences
• Develop automated reconciliation across orders, executions, positions, cash balances, and broker statements
• Support quantitative researchers with clean, consistent, point-in-time datasets for research and backtesting
• Support risk teams with account-level and portfolio-level monitoring data
• Develop internal data services, APIs, dashboards, alerts, and automated reports
• Improve database performance, system scalability, monitoring, and fault recovery
• Maintain clear documentation, data definitions, lineage, and operational procedures
• Collaborate with traders, quantitative researchers, risk managers, and software engineers

Core Qualifications

• Strong Python and SQL skills
• Experience with relational databases and data modeling
• Understanding of ETL/ELT pipelines, data validation, and production data workflows
• Ability to work with APIs, structured files, streaming data, and large datasets
• Familiarity with Linux, Git, testing, logging, and debugging
• Strong attention to accuracy, consistency, and operational reliability
• Ability to investigate data discrepancies and follow problems through to resolution
• Professional communication skills in either English or Mandarin; bilingual ability is welcome but not required

Financial Data Experience We Value

• Market prices, quotes, trades, and order-book data
• Orders, executions, positions, and account activity
• Futures, equities, ETFs, options, or other financial instruments
• P&L, margin, exposure, drawdown, and risk data
• Corporate actions, contract specifications, and futures rollover
• Time-series data and point-in-time historical datasets
• Broker statements and transaction reconciliation
• Reference data, symbol mapping, calendars, and time-zone normalization

Preferred Technical Experience

• PostgreSQL, TimescaleDB, ClickHouse, or other time-series and analytical databases
• Kafka, Redpanda, Redis, Airflow, Dagster, or similar data infrastructure
• Pandas, Polars, PySpark, or other data-processing frameworks
• IBKR API, broker APIs, exchange feeds, or institutional market-data platforms
• Real-time streaming systems and event-driven architecture
• Database optimization, partitioning, indexing, and high-volume ingestion
• Data monitoring, alerting, reconciliation, and recovery systems

What We Evaluate

• Ability to design accurate and maintainable financial data pipelines
• Understanding of the difference between event time, processing time, and exchange timestamps
• Approach to missing, duplicated, delayed, or corrected market data
• Ability to prevent look-ahead bias and data leakage in research datasets
• Understanding of financial data reconciliation and auditability
• Ability to balance system performance with data accuracy and reliability
• Ownership of previous projects and ability to explain technical decisions clearly

What We Offer

• The opportunity to build financial data infrastructure from the ground up
• Direct exposure to real trading, quantitative research, and risk-management workflows
• Meaningful ownership based on experience and technical ability
• Collaboration with traders, quantitative researchers, risk managers, and engineers
• Competitive compensation and performance-based growth opportunities
• A collaborative workplace that welcomes candidates from all backgrounds