Big Data Hadoop Architect Hybrid
About The Position
We are hiring a hands-on Big Data Hadoop Architect to lead the design and modernization of enterprise-scale data platforms. The role balances strategy with execution, drives cloud transformation, and builds scalable, cost-efficient data ecosystems. The architect will define the enterprise data architecture roadmap, lead platform and cloud migrations, establish data pipelines and lakehouse architectures, and guide governance, security, reliability, and performance practices. The position also provides technical leadership to engineering teams and works with business stakeholders, DevOps teams, and vendors on platform solutions and long-term investments.
Requirements
- 10–15+ years of IT experience, with at least 5 years in Big Data Architecture.
- Strong expertise in the Hadoop ecosystem, including HDFS, YARN, Hive, and HBase.
- Strong expertise in Apache Spark, including Core, SQL, and Streaming.
- Experience with Kafka, Pulsar, or Flink.
- Hands-on experience with at least one cloud platform: AWS (EMR, S3, Glue, Redshift), Azure (Data Lake, Synapse, HDInsight), or GCP (Dataproc, BigQuery).
- Strong programming skills in Java, Scala, or Python.
- Deep understanding of distributed systems, parallel processing, and data partitioning.
- Experience with containerization using Docker and Kubernetes.
- Expertise in data warehousing and ETL frameworks.
Nice To Haves
- Experience with Databricks, Snowflake, or modern lakehouse platforms.
- Exposure to AI/ML pipelines and feature engineering systems.
- Cloud certifications at the AWS, Azure, or GCP Architect level.
- Strong leadership and stakeholder management experience.
Responsibilities
- Define an enterprise data architecture roadmap aligned with business goals.
- Architect and implement large-scale Hadoop- and Spark-based data platforms.
- Lead migration from on-premises Hadoop clusters to cloud-native architectures.
- Design real-time and batch data pipelines using technologies such as Kafka, Spark Streaming, and Flink.
- Establish data lake and lakehouse architectures using technologies such as Delta Lake, Iceberg, and Hudi.
- Drive data modeling standards, including dimensional, data vault, and normalized models.
- Build ETL/ELT data ingestion frameworks for structured and unstructured sources.
- Optimize cluster performance, costs, and resource utilization.
- Define and enforce data governance, lineage, cataloging, and quality frameworks.
- Implement security best practices, including encryption, IAM, data masking, and applicable compliance requirements.
- Lead capacity planning and scalability strategies for petabyte-scale data.
- Evaluate and introduce modern tools beyond the Hadoop ecosystem, such as Databricks and Snowflake.
- Work with DevOps teams to implement CI/CD pipelines for data workflows.
- Provide technical leadership, conduct code reviews, and mentor engineering teams.
- Collaborate with business stakeholders to translate requirements into technical solutions.
- Drive incident resolution and root cause analysis for production data issues.
- Ensure high availability and disaster recovery strategies are in place.
- Influence tooling, vendor selection, and long-term data platform investments.
Benefits
- Comprehensive medical, dental, and vision insurance.
- 401(k) with employer match of up to 5–6%.
- Paid time off of 20–25 days and paid holidays.
- Parental leave of 12–16 weeks.
- Learning budget of $2,500 per year for certifications and courses.
- One-time home office setup allowance of $1,000.
- Wellness programs, including gym reimbursement and mental health support.
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What This Job Offers
Job Type
Full-time
Career Level
Senior
Education Level
No Education Listed
