Data Engineer

Build and operate production-grade batch and streaming data pipelines across SQL Server, cloud applications, device/event streams, Customer Relationship Management systems, and third-party sources.

10+ years of hands-on experience in data engineering, data warehousing, or data platform development.

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Strong production experience building and supporting end-to-end data pipelines.

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Expert SQL, including complex queries, query-plan analysis, and performance tuning.

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Strong development skills in Python, Java, or Scala.

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Hands-on experience with Apache Iceberg, Delta Lake, or Apache Hudi.

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Experience with distributed query technologies such as Trino, Presto, or Spark SQL.

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Hands-on Snowflake experience, including data modeling, performance optimization, external/Iceberg tables, secure data sharing, or access management.

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Experience with streaming and Change Data Capture technologies such as Apache Flink, Kafka, Spark Structured Streaming, or Debezium.

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Strong understanding of dimensional modeling, data quality, schema evolution, and data contracts.

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Experience with Kubernetes, containers, Continuous Integration and Continuous Deployment, and infrastructure-as-code.

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Experience implementing monitoring, alerting, lineage, reconciliation, and operational support for production data platforms.

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Ability to independently troubleshoot complex production issues and drive them through resolution., Experience with multi-tenant Software as a Service data platforms and customer data isolation.

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Experience integrating open lakehouse platforms with Snowflake.

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Azure cloud experience.

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Experience with Airflow, Dagster, dbt, Power BI, Looker, or Superset.

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Experience with data governance, cataloging, masking, and access-control frameworks.

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Exposure to Internet of Things/device telemetry, physical security, commercial real estate, or property technology.

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Experience using Artificial Intelligence-assisted engineering tools to accelerate data development., Bachelor’s or Master’s Degree in Computer Science, Computer or Electrical Engineering, Mathematics, or a related field.

Develop ingestion, transformation, and enrichment pipelines using Apache Flink, Kafka, Change Data Capture, SQL, and Python/Java/Scala.

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Implement lakehouse data layers across landing, raw, conformed, and consumption zones using Apache Iceberg and Trino.

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Build dimensional and analytical models for entities such as buildings, tenants, credentials, devices, and people.

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Implement robust handling for Change Data Capture, late-arriving data, retries, backfills, replay, schema evolution, and data reconciliation.

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Build and maintain integration between the lakehouse and Snowflake, including Iceberg-backed/external tables, secure sharing, and downstream data contracts.

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Develop automated data-quality checks, pipeline monitoring, lineage, freshness alerts, and operational dashboards.

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Troubleshoot and resolve production data issues, pipeline failures, performance bottlenecks, and data-quality problems.

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Optimize SQL queries, storage layouts, partitioning, clustering, compute utilization, and overall platform cost.

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Implement security controls including role-based access, row/column-level security, masking, and protection of sensitive data.

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Build and maintain Continuous Integration and Continuous Deployment pipelines, automated testing, infrastructure-as-code, and environment promotion processes.

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Participate in production support, incident resolution, Root Cause Analysis, deployment, and on-call activities.

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Work closely with Product, Application Engineering, Quality Assurance, DevOps/Site Reliability Engineering, Analytics, and globally distributed engineering teams.

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Contribute to technical design decisions and documentation while remaining primarily hands-on in implementation and production delivery.

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