Unlock Your Potential as a Senior Data Architect

1 day ago

Bengaluru, Karnataka, India beBeeDataEngineer Full time ₹ 10,90,400 - ₹ 1,63,51,600

We're seeking a Data Architect to lead the design and implementation of our internal data observability platform. This position involves collaborating with cross-functional teams to integrate analytics services with upstream annotation tools and downstream ML validation systems.

About the Role:

The successful candidate will have experience designing scalable batch and real-time data pipelines across structured and unstructured sources, as well as integrating analytics and observability services with upstream annotation tools and downstream ML validation systems.

Key Responsibilities:
  • Data Pipeline Design: Design and build scalable batch and real-time data pipelines using tools like AWS Glue, PySpark, or Airflow.
  • Integration and Collaboration: Integrate analytics and observability services with upstream annotation tools and downstream ML validation systems to enable full-cycle traceability.
  • Event Modeling: Collaborate with product, platform, and analytics teams to define event models, metrics, and data contracts.
  • Data Quality and Reconciliation: Develop ETL/ELT workflows ensuring data quality, lineage, and reconciliation.
  • Pipeline Monitoring and Alerting: Implement observability pipelines and alerts for mission-critical metrics (e.g., annotation throughput, quality KPIs, latency).
  • Data Analysis and Insights: Build data models and queries to power dashboards and insights via tools like Athena, QuickSight, or Redash.
  • Infrastructure Development: Contribute to infrastructure-as-code and CI/CD practices for deployment across cloud environments (preferably AWS).
  • Documentation and Support: Document architecture, data flow, and support runbooks; continuously improve platform performance and resilience.
Requirements:

To be successful in this role, you'll need:

  • A strong background in data engineering or backend development in data-intensive environments.
  • Proficiency in Python and SQL, with familiarity with PySpark or other distributed processing frameworks.
  • Experience with cloud-native data tools and services (S3, Lambda, Glue, Kinesis, Firehose, RDS).
  • Familiarity with frameworks like Apache Hadoop, Apache Spark, and related tools for handling large datasets.
  • Knowledge of data lake and warehouse patterns (e.g., Delta Lake, Redshift, Snowflake).
  • Solid understanding of data modeling, schema design, and versioned datasets.
  • Experience with data governance policies and security measures.
  • Proven experience in building resilient, production-grade pipelines and troubleshooting live systems.
  • Working knowledge of messaging frameworks like Kafka, Firehose etc
  • Good working knowledge of Database fundamentals, relational databases and SQL

Prior experience with observability/monitoring systems, data governance, RBAC, PII redaction, or compliance in analytics platforms is a plus.

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