Data Engineer

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

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