Senior Data Engineer

Reailize•Brookhaven, GA

About The Position

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. The role owns delivery from data ingestion through dashboards and serves as the main technical contact for the customer's data and engineering teams. The engineer will work with a distributed team that uses AI-assisted development in its daily work. Candidates must be based in the United States and available to travel to customer locations as required. The role includes working on site with the customer for workshops, design sessions, and key milestones. During interviews, assessments, and take-home assignments, candidates must work independently and may not use AI tools, third-party services, coaching platforms, or content-farming services unless explicitly permitted. B-Yond will communicate when AI tools are allowed for specific assessments.

Requirements

At least 6 years of data engineering experience, including at least 3 years building production pipelines over large-scale datasets.
Advanced SQL skills, including window functions, complex joins, query optimization, and incremental patterns.
Hands-on BigQuery experience.
Strong Python skills for data processing, automation, and testing.
Solid dimensional modeling experience, including 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 such as Tableau or Power BI.
Working knowledge of the wireless telecom domain, including how a mobile operator is organized and its main network and operations functions.
Experience across the full software development lifecycle, including requirements, design, implementation, testing, release, and support.
Experience with automated data testing, including unit tests for transformations, schema and data contract tests, data quality assertions, and regression checks.
Experience with CI/CD for data pipelines, including Git workflows, pull request reviews, and automated deployments across environments.
Practical experience using AI-assisted development tools such as Claude Code, GitHub Copilot, or Cursor for coding, testing, documentation, and review, with good judgment in validating and securing AI-generated output.
Experience working within enterprise client policies on security, compliance, and data governance.
Good English communication skills and comfort working directly with enterprise customers.
Based in the United States and able to travel to customer sites.

Nice To Haves

Experience with transformation frameworks such as Dataform or dbt.
Experience with distributed processing such as Spark (PySpark).
Experience with DataOps and enterprise data science platforms such as Domino, Databricks, or similar, including MLOps workflows.
Experience with orchestration tools such as Airflow.
Experience with data lake and lakehouse architectures, including Parquet and Delta/Iceberg.
Experience with cloud data platforms such as GCP, AWS, or Azure.
Experience with Docker and Kubernetes.
Experience with infrastructure as code such as Terraform.
Experience with semantic-layer design such as LookML and additional BI tools such as Grafana or Superset.
Experience handling sensitive or regulated data, including masking, access controls, and privacy requirements.

Responsibilities

Lead the design, build, and operation of scalable data transformation pipelines over large data volumes.
Design analytical data models, including star schemas, fact and dimension tables, and aggregation layers, for 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 handling late-arriving data, deduplication, reprocessing, and backfills.
Define and run data quality checks, validation rules, and pipeline observability, including monitoring, alerting, and lineage.
Optimize pipelines and queries for performance and cost.
Work with stakeholders to translate business requirements into data models and metric definitions.
Set project engineering standards for code reviews, branching, testing, CI/CD, and documentation, aligning them with client security, data governance, and engineering policies.
Work on site with the customer for workshops, design sessions, and key milestones.
Mentor other engineers and assist with estimation, planning, and technical risk management.