OKAYA INFOCOM · 6 hours ago
AWS Data Engineering Lead--New York, NY--Full Time
New York, NY
Full-time
Onsite
Senior Level, Lead/Staff
4+ years expOKAYA INFOCOM is seeking an AWS Data Engineering Lead to manage data engineering services and oversee cloud data platforms. The role involves designing and operating data pipelines, ensuring data quality and governance, and collaborating with stakeholders to deliver reliable datasets for analytics and AI applications.
ConsultingInformation TechnologySoftware
Hiring Manager
Sanjeev Kumar
Responsibilities
Ingest and model data from APIs, files/SFTP, and relational sources; implement layered architectures (raw/clean/serving) using PySpark/SQL and dbt, Python
Design and operate pipelines with Prefect (or Airflow), including scheduling, retries, parameterization, SLAs, and well documented runbooks
Build on cloud data platforms, leveraging S3/ADLS/GCS for storage and a Spark platform (e.g., Databricks or equivalent) for compute; manage jobs, secrets, and access
Publish governed data services and manage their lifecycle with Azure API Management (APIM) authentication/authorization, policies, versioning, quotas, and monitoring
Enforce data quality and governance through data contracts, validations/tests, lineage, observability, and proactive alerting
Optimize performance and cost via partitioning, clustering, query tuning, job sizing, and workload management
Uphold security and compliance (e.g., PII handling, encryption, masking) in line with firm standards
Collaborate with stakeholders (analytics, AI engineering, and business teams) to translate requirements into reliable, production ready datasets
Enable AI/LLM use cases by packaging datasets and metadata for downstream consumption, integrating via Model Context Protocol (MCP) where appropriate
Continuously improve platform reliability and developer productivity by automating routine tasks, reducing technical debt, and maintaining clear documentation
Qualification
Required
AWS Data Engineering Services (EMR/Glue, Redshift, Aurora, S3, Lambda), Spark, Python, Collibra, Snowflake/Databricks, Tableau
Ingest and model data from APIs, files/SFTP, and relational sources; implement layered architectures (raw/clean/serving) using PySpark/SQL and dbt, Python
Design and operate pipelines with Prefect (or Airflow), including scheduling, retries, parameterization, SLAs, and well documented runbooks
Build on cloud data platforms, leveraging S3/ADLS/GCS for storage and a Spark platform (e.g., Databricks or equivalent) for compute; manage jobs, secrets, and access
Publish governed data services and manage their lifecycle with Azure API Management (APIM) authentication/authorization, policies, versioning, quotas, and monitoring
Enforce data quality and governance through data contracts, validations/tests, lineage, observability, and proactive alerting
Optimize performance and cost via partitioning, clustering, query tuning, job sizing, and workload management
Uphold security and compliance (e.g., PII handling, encryption, masking) in line with firm standards
Collaborate with stakeholders (analytics, AI engineering, and business teams) to translate requirements into reliable, production ready datasets
Enable AI/LLM use cases by packaging datasets and metadata for downstream consumption, integrating via Model Context Protocol (MCP) where appropriate
Continuously improve platform reliability and developer productivity by automating routine tasks, reducing technical debt, and maintaining clear documentation
4–15 years of professional data engineering experience
Strong Python, SQL, and Spark (PySpark) skills, and/or Kafka
Snowflake (Snowpipe, Tasks, Streams) as a complementary warehouse
Databricks (Delta formats, workflows, cataloging) or equivalent Spark platforms
Hands-on experience building ETL/ELT with Prefect (or Airflow), dbt, Spark, and/or Kafka
Experience onboarding datasets to cloud data platforms (storage, compute, security, governance)
Familiarity with Azure/AWS/GCP data services (e.g., S3/ADLS/GCS; Redshift/BigQuery; Glue/ADF)
Git-based workflows CI/CD and containerization with Docker (Kubernetes a plus)
Strategic Technical Leadership: Defining data architecture, evaluating new technologies, and setting technical standards for AWS-based pipelines
Stakeholder Communication: Bridging the gap between technical teams and business stakeholders, gathering requirements, and reporting progress
Risk Management: Proactively identifying potential bottlenecks in data workflows, security risks, or scalability issues
Operational Excellence: Implementing automation, optimizing costs, and maintaining high data quality standards
Company
OKAYA INFOCOM
OKAYA was established in 2006 with the mission to enable success for our partners through trust and commitment.
201-500 employees
Funding
Current Stage
Growth StageLeadership Team
Akshay Gupta
CEO
Company data provided by crunchbase