Data Engineer - building production pipelines

Senior Data Engineer - 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.

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.

Qualifications:

• 6+ years in data engineering, including 3+ years building production pipelines over large-scale datasets.
• Advanced SQL: window functions, complex joins, query optimization and incremental patterns.
• Hands-on BigQuery experience.
• Strong Python for data processing, automation and testing.
• Solid dimensional modeling: 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.
• Automated testing for data: unit tests for transformations, schema and data contract tests, data quality assertions, regression checks.
• CI/CD for data pipelines: 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.

Nice to have

• Transformation frameworks: Dataform, dbt.
• Distributed processing: Spark (PySpark).
• DataOps and enterprise data science platforms such as Domino, Databricks or similar, including MLOps workflows.
• Orchestration: Airflow.
• Data lake and lakehouse architectures (Parquet, Delta/Iceberg).
• Cloud data platforms (Google Cloud Platform, AWS or Azure).
• Docker and Kubernetes.
• Infrastructure as code (Terraform).
• Semantic-layer design (for example LookML) and additional BI tools (Grafana, Superset).
• Experience handling sensitive or regulated data (masking, access controls, privacy requirements).