Cloud Engineer (Platform Analytics & Reporting) | Remote | 4-month contract
Important: This is a cloud engineering role, not a data analytics or data science position. The focus is building and operating pipelines, infrastructure, and dashboards in Go and AWS — not statistical analysis, modeling, or deriving insights.
Vervint is hiring a Mid-Level Cloud Engineer for our backend platform team. This is a hands-on, contract engineering role: you'll build and maintain the cloud pipelines that turn device and activity event data into curated tables and dashboards, working in Go and AWS alongside senior engineers who own the architecture.
What You'll Do:
Build ingestion jobs that move device, activity, and history events from existing streams into the data lake, written to the agreed format and partition layout.
Implement transforms and curated tables on top of raw event data, keeping catalog definitions in step with schemas as new device families and firmware revisions appear.
Write and tune SQL queries that hit partitions instead of scanning everything, plus the views and tables behind commonly asked questions.
Build dashboards and datasets for product, engineering, and support — activation, connectivity health, feature adoption, rollout progress, notification delivery — and keep refreshes working.
Add the AWS resources your work needs in code, following the existing stack, naming, tagging, and IAM patterns rather than inventing new ones.
Write the Go pieces the pipeline needs — stream consumers, transforms, Lambda functions, backfill jobs — with unit tests, keeping lint and the test suite green before opening a pull request.
Run and verify backfills and reprocessing carefully, including duplicates, late-arriving records, and the cost of a job that reads more than it needs to.
Take part in code review in both directions, take architectural direction from senior engineers, and raise gaps or ambiguous requirements early rather than guessing.
What You'll Bring:
3+ years building data pipelines or analytics systems on AWS, with hands-on S3, Athena, and DynamoDB experience.
Strong SQL — joins, window functions, aggregation over large event tables — and the habit of checking what a query actually scans before shipping it.
Working knowledge of columnar storage and partitioning: why Parquet beats JSON, how partition keys shape query cost, and what small files do to performance.
Hands-on experience in at least one compiled or scripting language used for data work — Go, Python, Java, or similar — and willingness to write Go in an existing codebase.
Experience consuming streaming or queued data (Kinesis, Firehose, SQS, Kafka, or equivalent) and handling retries, duplicates, and out-of-order records properly.
Exposure to infrastructure as code — CDK, CloudFormation, or Terraform — and a basic grasp of IAM, KMS, and least-privilege access.
Experience building dashboards or reports someone else relies on, in QuickSight or a comparable BI tool, and the care to make the numbers correct before making them pretty.
Clear written communication, the habit of surfacing blockers early, and the curiosity and adaptability to grow with the team.
Preferred Qualifications:
Experience with the Glue Data Catalog, Glue jobs, dbt, Step Functions, Airflow, or open table formats such as Iceberg on Athena.
Prior work with IoT or connected-device telemetry — device state, connectivity, firmware and OTA reporting — or other high-volume event data.
Familiarity with Go modules and monorepo workflows, or with OpenSearch, Prometheus, Loki, or OpenTelemetry.
Awareness of consumer data privacy requirements: deletion requests, PII minimization, and access-scoped analytics.
Comfort using AI-assisted development tools to move faster without shipping code or queries you can't explain.