Lead Technology Engineer, Technology Engineering

National Grid is hiring a Lead Data Engineer / Data Integration Lead for our AMI Process & Performance department in any of our National Grid US locations in Upstate NY, or MA.

Every day we deliver safe and secure energy to homes, communities, and businesses. We are there when people need us the most. We connect people to the energy they need for the lives they live.

pace of change in society and our industry is accelerating, and our expertise and track record put us in an unparalleled position to shape the sustainable future of our industry. To be successful we must anticipate the needs of our customers, reducing the cost of energy delivery today and pioneering the flexible energy systems of tomorrow. This requires us to deliver on our promises and always look for new opportunities to grow, both ourselves and our business.

We're hiring a Lead Data Engineer / Data Integration Lead to own and evolve the AMI Operations Center data backbone. This is a hands-on senior IC role focused on building reliable, scalable data pipelines and curated data models in Snowflake and the AMI data ecosystem (Azure + integration platforms) that power downstream analytics, operational dashboards, and automation. You will operate with high autonomy-framing ambiguous problems, clarifying requirements with stakeholders, proposing solution options and tradeoffs, and delivering production-ready data products end to end (ingestion modeling quality monitoring consumption). You will design and implement ELT/ETL patterns, enforce data quality and governance controls, and partner with IT/operations teams to productionize and continuously improve data products.

• Design, build, and support production-grade data pipelines (batch and near real-time where applicable) to ingest AMI meter, network, and operational data into Snowflake and related data stores.
• Develop and maintain curated dimensional/analytical models (e.g., star/snowflake schemas) and semantic-ready datasets to enable consistent reporting and self-service analytics without ad hoc Excel workarounds.
• Implement robust ELT/ETL patterns (SQL + Python) including incremental loads/CDC strategies, idempotent processing, and backfill/replay mechanisms.
• Build and optimize Snowflake objects and workloads (schemas, roles, warehouses, clustering/partitioning strategies, query tuning) with a focus on performance, cost, and reliability.
• Orchestrate and schedule pipelines using National Grid/AMI tooling (e.g., Matillion) and integration platforms/services (e.g., GoAnywhere MFT, MuleSoft, and Azure services such as Blob Storage, Logic Apps, Service Bus/Event Hub, and API Management), establishing standards for deployment, retries, alerting, and operational runbooks.
• Implement automated data quality controls (tests, validation rules, anomaly checks) and ensure data observability/monitoring for freshness, completeness, and SLA adherence.
• Partner with BI/reporting teams (e.g., Power BI) by delivering governed, well-documented datasets and semantic-friendly models-prioritizing reusable data products over one-off reports.
• Provide technical leadership through solution design, code review, and documentation; collaborate with Data Engineering and IT teams to align standards for security, access controls, lineage, and lifecycle management.
• Drive a "data product" mindset: proactively identify high-value AMI operational problems, translate them into clear data requirements, define success metrics/SLAs, and deliver reusable datasets that reduce manual effort and improve operational decision-making.
• Leverage automation (scripting, workflow tools, and approved AI assistants where appropriate) to reduce manual operational effort and improve mean-time-to-detect/resolve data issues.

Required qualifications

• 8+ years of hands-on data engineering experience delivering production pipelines and curated datasets (not primarily ad hoc reporting).
• Expert SQL skills including complex joins, window functions, performance tuning, and data profiling/validation.
• Strong Snowflake experience: data loading patterns, warehouse sizing/cost awareness, security (roles/grants), and performance concepts; solid grounding in relational database fundamentals.
• Proficiency with Python (or similar) for data engineering (APIs, file handling, transformations), and comfort working in Linux/CLI environments.
• Experience building and operating pipelines using modern ELT/ETL and integration tooling in the AMI ecosystem (e.g., Matillion, GoAnywhere MFT, MuleSoft, and/or Azure data/integration services).
• Experience designing analytical data models and enforcing data quality (tests/validation, reconciliation, documentation) for operationally critical datasets.
• Working knowledge of software engineering practices: version control (Git), code reviews, environment promotion, and supporting data products in production.
• Demonstrated ability to work independently in a complex environment: proactively discover requirements, navigate ambiguity, make sound technical decisions, and communicate tradeoffs to technical and non-technical stakeholders.

Preferred qualifications

• Experience with data governance practices (metadata, lineage, access controls) and partnering with product/business stakeholders to define data products and SLAs.
• Hands-on experience designing/implementing integrations using MuleSoft and/or managed file transfer patterns (e.g., GoAnywhere), including secure connectivity, error handling, and operational support.

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