Build data pipelines: Develop robust batch and streaming pipelines (Kafka/Kinesis) to ingest, transform, and enrich large-scale event data (impressions, clicks, conversions, costs, identity signals).
Create data aggregates & marts: Design and maintain curated aggregates and dimensional models for multiple consumers—prediction models, agents, BI dashboards, and measurement workflows.
Data modeling & contracts: Define schemas, data contracts, and versioning strategies to keep downstream systems stable as sources evolve.
Data quality & reliability: Implement validation, anomaly detection, backfills, and reconciliation to ensure completeness, correctness, and timeliness (SLAs/SLOs).
Performance & cost optimization: Optimize compute/storage for scale (partitioning, file sizing, incremental processing, indexing), balancing latency, throughput, and cost.
Orchestration & automation: Build repeatable workflows with scheduling/orchestration (e.g., Airflow, Dagster, Step Functions) and CI/CD for data pipelines.
Observability for data systems: Instrument pipelines with metrics, logs, lineage, and alerting to accelerate detection and root-cause analysis of data issues.
Security & governance: Apply least-privilege access, PII-aware handling, and governance controls aligned with enterprise standards.

5+ years building production data pipelines and data products (batch and/or streaming) in a high-scale environment.
Strong experience with SQL and data modeling (dimensional modeling, star/snowflake schemas, event modeling).
Hands-on experience with streaming systems (Kafka preferred) and/or AWS Kinesis, including event-driven designs.
Proficiency in one or more languages used for data engineering (Python, Java, Scala, or Go).
Experience with distributed data processing (Spark, Flink, or equivalent) and performance tuning at scale.
Experience with AWS data services and cloud-native patterns (S3, Glue/EMR, Athena, Redshift, etc. as applicable).
Familiarity with lakehouse/table formats and large-scale storage patterns (e.g., Parquet; Iceberg/Hudi/Delta are a plus).
Experience with orchestration/workflow tooling (Airflow/Dagster/Step Functions) and CI/CD for data workloads.
Strong data quality/observability practices (tests, monitoring, lineage; understanding of SLAs/SLOs).
Experience with SQL + NoSQL data stores (e.g., Postgres/MySQL; DynamoDB/Cassandra/Redis) and choosing the right store per use case.
Clear communicator and collaborator; able to work with mixed audiences and translate needs into reliable data interfaces.

Senior Data Engineer

Key skills