AWS Data Engineering

Strong ownership of production data platforms, sound architectural judgment, and an ability to translate ambiguous needs into practical designs.

Clear communication, collaborative technical leadership, and a commitment to mentoring, documentation, and engineering quality.

Experience connecting event data, analytics, and production ML workflows while balancing reliability, performance, and cost.

Key Responsibilities

Build scalable pipelines on AWS using Airflow (MWAA), Glue, Kafka, S3, and Redshift; evolve event schemas, taxonomy, versioning, and migrations.

Establish Data Quality and observability through schema validation, instrumentation testing, anomaly detection, freshness monitoring, completeness checks, and lineage.

Correctly process late-arriving and out-of-order events using lookback reprocessing and idempotent incremental loads.

Develop personalization datasets and feature pipelines, including a batch and low-latency Feature Store using Redshift or S3 to DynamoDB serving patterns.

Support AWS Batch and SageMaker workflows through training datasets, offline evaluation data, and model-output delivery to downstream APIs.

Apply dimensional and event Data Modeling, instrument AI interaction and feedback events, optimize warehouse workloads, and establish code review, testing, CI/CD, documentation, and on-call standards.

Required Qualifications

8+ years in data engineering, including ownership of production pipelines at scale, with strong SQL and Python skills.

Deep experience with AWS services including S3, Glue, Redshift, DynamoDB, Lambda, and IAM.

Production Apache Airflow experience covering DAG design, dependencies, templating, alerting, and backfills.

Streaming ingestion experience with Kafka, Kinesis, SQS, or comparable technologies, plus product analytics data and observability frameworks.

Experience supporting production ML through feature engineering, training datasets, feature stores, or model-serving data flows.

Preferred Qualifications

Experience with PySpark or Spark, recommendation systems, embeddings, and Embedding pipelines supporting candidate generation or ranking.

Familiarity with LLM and Generative AI applications, vector stores, retrieval pipelines, evaluation data, and feedback loops.

Exposure to MLOps tooling, model registries, experiment tracking, instrumentation governance, A/B testing, and experiment metric pipelines.

Experience with Iceberg, Delta, Hudi, or Redshift data sharing.

Thinking Minds’ client offers architectural ownership, collaborative technical work, medical, dental, and vision coverage, retirement matching, paid time off, tuition reimbursement, lifestyle benefits, and relocation assistance.