Note: The job is a remote job and is open to candidates in USA. Finstock, Inc. is a company that builds AI-powered financial research and market intelligence tools. They are seeking a Data Engineer to design, develop, test, and operate data pipelines that manage financial and market-related data for their platform.
Responsibilities
- Design, build, and maintain scalable data pipelines for financial market data, company fundamentals, filings, corporate actions, news, research metadata, analytics outputs, and product usage data
- Develop robust ETL/ELT workflows for batch and near-real-time data processing
- Build and maintain data models, data marts, warehouse tables, and analytical datasets used by product, research, AI, and engineering teams
- Implement data quality checks, validation rules, reconciliation workflows, anomaly detection, and automated monitoring
- Improve data reliability, latency, lineage, observability, and documentation across Finstock's data infrastructure
- Integrate data from APIs, vendor feeds, public sources, internal systems, and approved third-party data providers
- Collaborate with analysts, product managers, AI engineers, backend engineers, and leadership to translate business and research requirements into reliable data products
- Support financial research workflows involving equities, ETFs, indices, FX, crypto assets, commodities, macro indicators, and cross-asset market intelligence
- Build secure data access patterns, permission controls, and audit-friendly workflows for sensitive or user-scoped data
- Maintain documentation for data sources, schemas, transformation logic, pipeline ownership, data quality assumptions, and known limitations
- Troubleshoot pipeline failures, data discrepancies, performance bottlenecks, and production incidents
- Contribute to cloud infrastructure, CI/CD workflows, testing standards, and engineering best practices for data systems
- Ensure data usage follows applicable licensing, confidentiality, security, privacy, and compliance requirements
Skills
- 3+ years of professional experience in data engineering, backend data systems, analytics engineering, or a related technical role
- Strong proficiency in SQL and Python
- Experience building and maintaining production-grade ETL/ELT pipelines
- Experience with cloud platforms such as AWS, Google Cloud Platform, or Microsoft Azure
- Experience with data warehouses or lakehouse technologies such as Snowflake, BigQuery, Redshift, Databricks, Delta Lake, or similar systems
- Familiarity with orchestration and transformation tools such as Airflow, Dagster, Prefect, dbt, or similar workflow tools
- Strong understanding of data modeling, schema design, partitioning, indexing, data quality testing, and pipeline observability
- Ability to work with APIs, structured data, semi-structured data, JSON, CSV, Parquet, relational databases, and time-series datasets
- Strong debugging, documentation, and communication skills
- Ability to work independently in a remote environment and collaborate effectively across product, engineering, and analyst teams
- Professional commitment to data security, confidentiality, and responsible handling of financial and user-related data
- Ability to work remotely in compliance with applicable laws and eligibility requirements
- Experience working with financial market data, trading analytics, investment research platforms, fintech products, or capital markets infrastructure
- Familiarity with equities, ETFs, indices, FX, crypto assets, commodities, financial statements, corporate actions, and market data vendors
- Experience with streaming or event-driven systems such as Kafka, Kinesis, Pub/Sub, or similar technologies
- Experience with data APIs, vector databases, search infrastructure, knowledge graphs, or retrieval systems used in AI-assisted products
- Experience with PostgreSQL, MySQL, MongoDB, Redis, Elasticsearch/OpenSearch, or time-series databases
- Experience with Docker, Kubernetes, Terraform, GitHub Actions, CI/CD, infrastructure-as-code, and production monitoring tools
- Experience supporting AI, machine learning, LLM, or analytics products with reliable data pipelines
- Familiarity with data governance, access control, audit trails, privacy controls, and vendor data licensing requirements
- Interest in financial research, market intelligence, quantitative analytics, and AI-assisted research workflows
Benefits
- Remote work with a distributed international team.
- A company email account and access to approved work tools, including Microsoft 365, Outlook, Teams, GitHub, and company-approved productivity tools, subject to internal security and usage policies.
- Opportunity to participate in company offsite activities, including possible Hong Kong offsites, subject to business schedule, travel eligibility, visa/documentation requirements, and company approval. Approved business-related travel, accommodation, and reasonable expenses will be covered by the company.
Company Overview