Staff Data Engineer - Gaming Industry (Latam)

X-Team; United States; Remote; USD 120,000 - 180,000; Full time

Job summary

X-Team is seeking a Staff Data Engineer for Latam remote engagement, focusing on Lakehouse architectures, data contracts, and end-to-end pipelines across Bronze, Silver, and Gold layers. You will partner with business stakeholders to translate needs into formal contracts, and drive ingestion from CDC, APIs, and web scrapers using Databricks, PySpark, and dbt.

You will shape production-grade pipelines, ensure performance, and contribute to clear, human-readable documentation, while participating

Qualifications

• Senior/Staff level data engineering experience designing, building, and maintaining production-grade data pipelines.
• Expert-level SQL and ability to diagnose slow joins and heavy queries.
• Strong coding experience in Python; other JVM languages considered.
• Hands-on Databricks/Spark/PySpark and dbt for batch and streaming workloads.
• Solid data modeling with Medallion architecture and star/snowflake schemas.

Responsibilities

• Own end-to-end data pipelines from business questions to production-ready data.
• Define data contracts with non-technical stakeholders before pipeline work begins.
• Design and maintain Bronze/Silver/Gold lakehouse architectures and models.
• Ingest from CDC, REST/GraphQL, relational/NoSQL databases, and web scrapers.
• Tune performance, remove slow joins, and optimize SQL and compute usage.
• Orchestrate DAGs in Airflow, manage CI/CD, and participate in on-call rotation.
• Create concise, human-readable technical documentation.

Skills

SQL Mastery

Python

Scala/Java

AI-assisted development

Data modeling

Stakeholder partnership

Tools

Databricks

Spark/PySpark

dbt

Apache Airflow

Astronomer

Kafka

Job description

Senior/Staff Data Engineer - Lakehouse & Data Contracts (Latam) #3166

Remote - Latam

The Mission

Great data pipelines start with clear business logic, not code. You'll partner directly with business stakeholders to define strict data contracts and lock down exact metric requirements before a single pipeline gets built. Once the specs are set, you'll own end-to-end batch and streaming pipelines across Bronze, Silver, and Gold layers in Databricks and dbt, from CDC and scraper ingestion all the way to star and snowflake consumption schemas. This is an AI-first team: you'll use LLMs every day to move faster, while holding a high bar for clean code and concise, human-written documentation.

The Role

As a Staff Data Engineer, you'll own the full path from business question to production-ready data.

• Business Data Contracts: Work with non-technical business teams to extract reporting needs and establish formal data contracts before pipeline construction starts.
• Medallion & Dimensional Modeling: Structure data across Bronze, Silver, and Gold layers, building normalized staging models and denormalized star/snowflake schemas (facts and dimensions) tuned for business metrics.
• Lakehouse Pipeline Engineering: Architect and maintain batch and streaming pipelines in Databricks, PySpark, and dbt.
• Multi-Source Ingestion: Pull operational data through Change Data Capture (CDC), REST/GraphQL APIs, relational and NoSQL databases, and custom web scrapers.
• Performance Tuning: Shape raw data, profile pipeline bottlenecks, and remove slow SQL joins and unnecessary compute overhead.
• Orchestration, CI/CD & On-Call: Manage DAG dependencies in Airflow (Astronomer), deploy through CI/CD pipelines, and take part in the on-call rotation to keep pipelines healthy.
• Human-Readable Documentation: Write concise, high-value technical documentation for your teammates, edited by you, not left as raw AI output.

The \"Must-Haves\"

• Experience: Senior / Staff level data engineering experience designing, building, and maintaining production-grade data pipelines and lakehouse architectures.
• SQL Mastery: Expert-level SQL is mandatory, including diagnosing and fixing slow joins and heavy queries.
• Programming: Python is strongly preferred; strong experience with Scala, Java, Kotlin, or C# is also considered.
• Lakehouse Stack: Hands-on experience with Databricks, Spark/PySpark, and dbt for both batch and streaming workloads.
• Data Modeling: Solid command of Medallion architecture and dimensional modeling (star/snowflake schemas, facts and dimensions).
• Data Ingestion: Proven experience with CDC, Kafka, REST/GraphQL APIs, Postgres, MongoDB, and web scraping.
• Orchestration & Deployment: Experience managing Apache Airflow DAGs and shipping through CI/CD, plus willingness to join an on-call rotation.
• Stakeholder Partnership: Demonstrated ability to turn non-technical business needs into clear data contracts and metric definitions.
• AI-Assisted Development: Daily, practical use of LLM tools such as Claude Code, ChatGPT, or Cursor for code generation, refactoring, and troubleshooting.
• Location: Strictly restricted to candidates residing in Latam, with the ability to maintain a minimum 6-hour daily working overlap with standard US time zones (PST/EST), along with active participation in an on-call rotation to maintain pipeline health and reliability.

The \"Bonus\" Skills

• Astronomer: Direct experience running Airflow on Astronomer.
• Alternative Orchestrators: Familiarity with Dagster or Prefect.
• Stream Processing: Experience with Apache Flink.
• Warehouses & Table Formats: Exposure to Snowflake, BigQuery Dataform, Delta Lake, or Apache Iceberg.
• Additional Databases: Experience with MySQL or DynamoDB.
• Business-Centric Mindset: You prioritize understanding the underlying business logic and stakeholder requirements before writing code.
• AI-Augmented Builder: You actively embrace modern AI tools (Claude Code, ChatGPT, Cursor) as daily multipliers to accelerate execution and troubleshooting, but review everything they produce with a critical eye.
• Data Quality Champion: You treat data accuracy and data contracts as non-negotiable foundations for reliable analytics.
• Clear Communicator: You bridge the gap between technical execution and business strategy, producing clear, human-readable documentation.
• Production Owner: You take full accountability for system reliability, performance tuning, and operational health.