Senior Data Engineer

Gemini•Miami, FL

2d•$126,000 - $180,000•Hybrid

About The Position

The Data team is responsible for designing and operating the data infrastructure that powers insight, reporting, analytics, and machine learning across the business. As a Senior Data Engineer, you will contribute to architectural decisions, mentor junior engineers, and build high-scale systems that have meaningful impact on your team and the teams you partner with. You will own the end-to-end delivery of data products within your domain, and partner closely with product, analytics, ML, finance, operations, and engineering teams to move, transform, and model data reliably, with observability, resilience, and agility.

Requirements

5+ years of experience in data engineering (or similar) roles
Strong experience in ETL/ELT pipeline design, implementation, and optimization
Deep expertise in Python and SQL writing production-quality, maintainable, testable code
Experience with large-scale data warehouses (e.g. Databricks, BigQuery, Snowflake)
Solid grounding in software engineering fundamentals, data structures, and systems thinking
Hands-on experience in data modeling (dimensional modeling, normalization, schema design)
Experience building systems with real-time or streaming data (e.g. Kafka, Kinesis, Flink, Spark Streaming), and familiarity with CDC frameworks
Experience with orchestration / workflow frameworks (e.g. Airflow)
Familiarity with data governance, lineage, metadata, cataloging, and data quality practices

Nice To Haves

Experience with crypto, financial services, trading, markets, or exchange systems
Experience with blockchain, crypto, Web3 data — e.g. blocks, transactions, contract calls, token transfers, UTXO/account models, on-chain indexing, chain APIs, etc.
Experience with infrastructure as code, containerization, and CI/CD pipelines
Hands-on experience managing and optimizing Databricks on AWS

Responsibilities

Design, build, and maintain data infrastructure and pipelines spanning both batch and real-time / streaming workloads, contributing to architectural decisions along the way
Build and maintain scalable, efficient, and reliable ETL/ELT pipelines using languages and frameworks such as Python, SQL, Spark, Flink, Beam, or equivalents
Work on real-time or near-real-time data solutions (e.g. CDC, streaming, micro-batch) for use cases that require timely data
Partner with data scientists, ML engineers, analysts, and product teams to understand data requirements, define SLAs, and deliver coherent data products that others can self-serve
Establish data quality, validation, observability, and monitoring frameworks (data auditing, alerting, anomaly detection, data lineage)
Investigate and resolve complex production issues: root cause analysis, performance bottlenecks, data integrity, fault tolerance
Document data flows, data dictionaries, architecture patterns, and operational runbooks