Description

Key Responsibilities

• Lead end-to-end data solution architecture (data flows, models, and system design).

• Define data platform patterns (batch vs streaming, ingestion, storage, processing).

• Build prototypes and guide teams in developing scalable data pipelines and models.

• Optimize data performance, partitioning, and query efficiency.

• Collaborate with business and engineering teams to translate requirements into solutions.

• Ensure security, governance, and compliance (IAM, encryption, data quality).

• Establish standards, CI/CD practices, and reusable frameworks.

• Drive observability, monitoring, and cost optimization for data platforms.

Must-Have Skills & Experience

• 6+ years in Data Engineering / Data Architecture.

• Strong expertise in data modeling (Dimensional, Data Vault, schema design).

• Hands-on experience with modern data stack:

o Cloud DW: Snowflake / Redshift / BigQuery

o Processing: Spark / Databricks / PySpark

o Streaming: Kafka / Kinesis

• Strong programming in Python (or Scala) + advanced SQL.

• Experience building enterprise-scale data pipelines.

• Hands-on with ETL/ELT, orchestration tools (Airflow, dbt, etc.).

• Experience with cloud platforms (AWS preferred).

• Knowledge of IAM, encryption, and data security best practices.

• Strong stakeholder communication & architecture decision-making skills.

Nice to Have

• Experience with Terraform, Kubernetes, Docker.

• Familiarity with data governance tools (lineage, cataloging, data quality).

• Certifications (AWS Architect, Databricks, Snowflake, CDMP).

• Exposure to observability tools (Datadog, Prometheus

Key Skills

Education

Bachelor's degree