Senior Data Engineer - Contract
Reailize
- Bellevue, United States
- Senior Level
- 6+ years
- Posted
Skills
Required
- data engineering
- window functions
- complex joins
- query optimization
- BigQuery
- data processing
- automation
- GitHub
- GCP / AWS / Azure
- Docker
- Kubernetes
- Terraform
Preferred
- Dataform
- Semantic-layer design
- LookML
- Grafana
- Superset
- dbt
- data transformation
- Design analytical data models
- star schema
- Domino
About the role B-Yond is hiring a Senior Data Engineer to lead the technical delivery of a data and analytics program for a large enterprise telecom customer. You'll own the work from ingestion through to dashboards. You'll be the main technical contact with the customer's data and engineering teams, and you'll work with a distributed team that uses AI-assisted development every day. This is a 6 months contract with a potential to extend. Please note that candidates must be based in the United States and available to travel to customer locations as required.
About Reailize
- Us Join Reailize – where innovation meets impact.
- We’re on a mission to help global enterprises work smarter, faster, and more efficiently at the cutting edge of AI, data, and cloud .
- By combining deep expertise, advanced engineering, and patented software, we transform complexity into opportunity.
- Here, curiosity, collaboration, and bold ideas aren’t just welcomed – they’re celebrated.
What makes us different?
We are a private & global group of companies with 20+ years of proven track record , supporting leading organizations in Telecom, Security, and Real Estate across North America, Europe, the Middle East, and Latin America .
Responsibilities
- Lead the design, build and operation of scalable data transformation pipelines over large data volumes.
- Design analytical data models (star schema, fact and dimension tables, aggregation layers) that serve reporting and dashboards.
- Build and maintain dashboards in Looker or similar BI tools, working with stakeholders on metrics, layout and usability.
- Build reliable incremental processing, including late-arriving data, deduplication, reprocessing and backfills.
- Define and run data quality checks, validation rules and pipeline observability (monitoring, alerting, lineage).
- Optimize pipelines and queries for performance and cost.
- Work with stakeholders to turn business requirements into data models and metric definitions.
- Set the engineering standards for the project (code reviews, branching, testing, CI/CD, documentation) and keep them aligned with the client's security, data governance and engineering policies.
- Work on site with the customer for workshops, design sessions and key milestones.
- Mentor other engineers and help with estimation, planning and technical risk management.
- unit tests for transformations, schema and data contract tests, data quality assertions, regression checks.
- Spark (PySpark).
- DataOps and enterprise data science platforms such as Domino, Databricks or similar, including MLOps workflows.
Requirements
- 6+ years in data engineering, including 3+ years building production pipelines over large-scale datasets.
- window functions, complex joins, query optimization and incremental patterns.
- Hands-on BigQuery experience.
- Python for data processing, automation and testing.
- star and snowflake schemas, fact and dimension tables, slowly changing dimensions, grain definition, aggregations and rollups.
- Hands-on experience building production dashboards in Looker or another BI tool (Tableau, Power BI or similar).
- Working knowledge of the wireless telecom domain.
- You don't need to be a telco expert, but you should understand how a mobile operator is organized and its main network and operations functions.
- Experience across the full software development lifecycle: requirements, design, implementation, testing, release and support.
- Git workflows, pull request reviews, automated deployments across environments.
- Practical use of AI-assisted development tools (for example Claude Code, GitHub Copilot or Cursor) for coding, testing, documentation and review, along with good judgment in validating and securing AI-generated output.
- Experience working within enterprise client policies on security, compliance and data governance.
- Good communication in English and comfort working directly with enterprise customers.
- Based in the United States and able to travel to customer sites.
- Airflow.
- Data lake and lakehouse architectures (Parquet, Delta/Iceberg).
- Cloud data platforms (GCP, AWS or Azure).
- Docker and Kubernetes.
- Infrastructure as code (Terraform).
- Experience handling sensitive or regulated data (masking, access controls, privacy requirements).
Preferred
- Dataform, dbt.
- Semantic-layer design (for example LookML) and additional BI tools (Grafana, Superset).
Interview Integrity
During the interview process, we're evaluating your individual problem-solving skills, creativity, and approach to challenges. While AI tools like ChatGPT, Claude, and Cursor are part of your daily toolkit once you join B-Yond, all interviews, assessments, and take-home assignments must be completed independently. You may not use AI tools, third-party services, coaching platforms, or content-farming services during any part of the interview process unless we explicitly permit it. We will clearly communicate when AI tools are allowed for specific assessments. Any indication of third-party assistance or AI-generated responses will result in immediate disqualification.
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