Data Engineer

Finstock, Inc. builds AI-powered financial research, trading analytics, quantitative research, and market intelligence tools. The Data Engineer will build and maintain scalable data infrastructure, including pipelines that ingest, transform, validate, and deliver financial and market-related data to internal systems, AI-assisted research tools, and product features.

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

Requirements:

• • 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