Data Infrastructure Specialist
6 hours ago
We are seeking a highly skilled Data Infrastructure Specialist to help us scale and enhance our internal data observability and analytics platform. This platform integrates with data annotation tools and machine learning pipelines to provide visibility, insights, and automation across large-scale data operations.
This role involves designing and building scalable batch and real-time data pipelines across structured and unstructured sources. You will integrate analytics and observability services with upstream annotation tools and downstream machine learning validation systems to enable full-cycle traceability.
As a Data Infrastructure Specialist, you will collaborate with product, platform, and analytics teams to define event models, metrics, and data contracts. You will develop ETL/ELT workflows using tools like AWS Glue, PySpark, or Airflow; ensure data quality, lineage, and reconciliation.
You will implement observability pipelines and alerts for mission-critical metrics such as annotation throughput, quality KPIs, latency. Additionally, you will build data models and queries to power dashboards and insights via tools like Athena, QuickSight, or Redash.
Other responsibilities include contributing to infrastructure-as-code and CI/CD practices for deployment across cloud environments (preferably AWS). You will also document architecture, data flow, and support runbooks; continuously improve platform performance and resilience.
To succeed in this role, you should have 3–8 years of experience in data engineering or backend development in data-intensive environments. Proficiency in Python and SQL is required, along with familiarity with PySpark or other distributed processing frameworks.
A strong understanding of cloud-native data tools and services (S3, Lambda, Glue, Kinesis, Firehose, RDS) is necessary. Experience with frameworks like Apache Hadoop, Apache Spark, and related tools for handling large datasets is also required. Knowledge of data lake and warehouse patterns (e.g., Delta Lake, Redshift, Snowflake) is preferred.
A solid understanding of data modeling, schema design, and versioned datasets is essential. Data Governance and Security: Understanding and implementing data governance policies and security measures is crucial. Proven experience in building resilient, production-grade pipelines and troubleshooting live systems is also required.
Additionally, working knowledge of messaging frameworks like Kafka, Firehose etc and API frameworks, robust and performant API design is necessary. Good working knowledge of Database fundamentals, relational databases and SQL is required. Having experience with observability/monitoring systems (e.g., Prometheus, Grafana, OpenTelemetry) is a plus.
Exposure to annotation/ML workflow tools or ML model validation platforms is desirable. Comfort working in Agile, distributed teams using tools like Git, JIRA, and Slack is also an asset.
You'll work at the intersection of AI, data infrastructure, and impact—contributing to platforms that ensure AI is explainable, auditable, and ethical at scale. Join a team building the next generation of intelligent data operations.
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