Mo Umer Data Engineer | Financial Market Data Infrastructure 5 Years | Python | SQL | Snowflake | BigQuery | dbt | Airflow/Dagster | Kafka/Kinesis | Financial Market Data | AI Research Infrastructure elijahha977@gmail.com | +1 (631) 571-9172 | Remote PROFESSIONAL SUMMARY Data engineer with 5+ years building and maintaining production-grade ETL/ELT pipelines, data warehouses, and data quality systems for financial and analytics platforms. Strong SQL and Python proficiency. Experience with cloud data warehouses including Snowflake, BigQuery, Redshift, and Databricks. Familiar with orchestration and transformation tools including Airflow, Dagster, Prefect, and dbt. Strong data modeling, schema design, partitioning, data quality testing, and pipeline observability. Experience with financial market data including equities, ETFs, indices, FX, crypto assets, commodities, financial statements, corporate actions, and macroeconomic data. Familiar with streaming systems including Kafka and Kinesis. Experience with data APIs, vector databases, and retrieval systems for AI-assisted research products. Experience with PostgreSQL, MySQL, Redis, time-series databases, and JSON/Parquet/CSV data formats. Familiar with Docker, Terraform, GitHub Actions, and CI/CD. Committed to data security, confidentiality, and responsible financial data handling. CORE COMPETENCIES Python & SQL: Strong Python and SQL for production ETL/ELT pipeline development, data transformation, quality validation, and financial data engineering workloads Snowflake, BigQuery & Databricks: Cloud data warehouse and lakehouse experience across Snowflake, BigQuery, Redshift, and Databricks for financial analytics infrastructure dbt, Airflow & Dagster: dbt for transformation modeling and data quality; Airflow and Dagster for pipeline orchestration across batch and near-real-time financial data workflows Financial Market Data: Equities, ETFs, indices, FX, crypto assets, commodities, financial statements, corporate actions, macroeconomic data; financial data vendor integration and quality Data Quality & Observability: Automated data quality checks, validation rules, reconciliation, anomaly detection, lineage tracking, and pipeline observability for financial data reliability Kafka, Kinesis & Streaming: Streaming and event-driven systems with Kafka and Kinesis for near-real-time financial market data ingestion and pipeline processing AI Research Infrastructure: Data APIs, vector databases, and retrieval systems supporting AI-assisted financial research and market intelligence product features Docker, Terraform & CI/CD: Docker, Terraform, GitHub Actions, and CI/CD for data infrastructure automation, reproducible environments, and production deployment governance PROFESSIONAL EXPERIENCE Senior Data Engineer Jan 2026 - Present Allata (Technology & AI Consulting) n Design, build, and maintain scalable data pipelines for financial market data including equities, ETFs, FX, crypto, commodities, financial statements, corporate actions, and macroeconomic indicators. n Develop ETL/ELT workflows with dbt and Airflow/Dagster for batch and near-real-time processing; build data models, data marts, and analytical datasets used by product, research, and AI teams in Snowflake and BigQuery. n Implement data quality checks, validation rules, reconciliation workflows, anomaly detection, lineage tracking, and automated monitoring ensuring financial data reliability and auditability. n Integrate data from financial market data APIs, vendor feeds, internal systems, and third-party providers; build secure data access patterns and audit-friendly workflows for sensitive financial data. Data Engineer Apr 2023 - Jan 2026 EBG (Energy & Analytics) n Built financial market data pipelines with Snowflake, BigQuery, and dbt transformation models for analytics and AI research platform infrastructure. n Implemented Airflow and Dagster orchestration with data quality frameworks, reconciliation workflows, and lineage tracking for financial data reliability. n Integrated financial data APIs, Kafka streaming, and vendor feeds; built secure data access patterns for sensitive market and user data. Data Engineer, Data Platform Jun 2018 - Mar 2023 Amplitude (Product Analytics Platform) n Built Python and SQL data pipelines for financial analytics and market intelligence platforms. n Implemented dbt transformation models and Airflow orchestration for batch data processing. n Contributed to data quality frameworks and pipeline observability for financial data systems. KEY PROJECTS Financial Market Data Infrastructure: Multi-Asset Ingestion, dbt Quality Modeling, and AI Research Pipeline Python, SQL, Snowflake, BigQuery, dbt, Airflow, Dagster, Kafka, Kinesis, financial market data (equities/ETFs/FX/crypto/commodities/fundamentals/corporate actions/macro), data quality/reconciliation/lineage, vector databases, AI research features, Docker, Terraform, GitHub Actions CI/CD n Problem: An AI-powered financial research and market intelligence platform needed scalable data pipelines ingesting cross-asset market data from financial data vendors and public sources into Snowflake and BigQuery, with dbt transformation models delivering clean datasets to product, research, and AI teams, data quality frameworks ensuring prices, fundamentals, and corporate actions were reliable, and retrieval infrastructure supporting AI-assisted research features. n Built: Designed multi-asset market data ingestion pipelines covering equities, ETFs, indices, FX, crypto assets, commodities, financial statements, corporate actions, and macroeconomic data from vendor APIs and public feeds. Built dbt transformation models with automated quality checks validating price completeness, corporate action integrity, and fundamental data consistency. Implemented Kafka and Kinesis streaming for near-real-time market data feeds. Deployed Airflow and Dagster DAGs for batch orchestration with monitoring and alerting. Built secure data access patterns with audit trails for sensitive financial and user-scoped data. Integrated vector database retrieval layer supporting AI-assisted research query features. Managed infrastructure with Docker, Terraform, and GitHub Actions CI/CD. n Deployed: Financial market data platform with multi-asset coverage serving product, research, and AI teams from clean, validated Snowflake and BigQuery datasets. dbt quality models prevent bad prices and incomplete fundamentals from reaching AI research features. Streaming pipelines maintain near-real-time data freshness for market intelligence tools. Data Quality, Reconciliation, and Lineage for Financial Data Reliability dbt, Python, Airflow, data quality checks, validation rules, reconciliation workflows, anomaly detection, lineage tracking, pipeline observability, financial data compliance n Challenge: Financial market data arriving from diverse vendor feeds and public sources carried price errors, corporate action gaps, and stale fundamental data that propagated undetected into analytics and AI research features, requiring systematic quality enforcement embedded in the pipeline rather than discovered downstream by analysts or AI consumers. n Designed: Built automated data quality framework with validation rules covering price sanity checks, completeness thresholds, corporate action timeline consistency, and fundamental data freshness. Implemented reconciliation workflows comparing vendor data to cross-source validation references. Deployed anomaly detection for price spike and fundamental data variance alerts. Built lineage tracking from source feed through transformation to analytics serving layer. Maintained documentation covering data sources, schemas, transformation logic, quality assumptions, and known vendor limitations. n Outcome: Data quality framework catches financial data errors at ingestion before reaching analytics and AI features. Reconciliation validates cross-source consistency for high-stakes financial metrics. Lineage documentation supports audit requirements and vendor SLA tracking. Quality documentation enables team confidence in data reliability for AI research tooling. EDUCATION & CERTIFICATIONS Bachelor of Science in Computer Science