EnablerMinds is a next-generation boutique company delivering end-to-end
Cloud, Data & AI solutions. Our elite team of data engineers, architects,
data scientists, AI engineers and industry specialists empowers enterprises to
modernize their data ecosystems, accelerate AI adoption, and unlock
transformative business value. With proven methodologies, enterprise-ready
frameworks, and an agile delivery and staffing model, we deliver
high-performance, scalable, and cost-efficient outcomes tailored to the needs
of tomorrow’s intelligent enterprise.
About the Role
We
are looking for a Cloud Data Engineer with hands-on experience in Azure,
AWS, or other cloud platforms to design, build, and scale robust data
pipelines and data lake architectures. This role requires deep
technical expertise in Big Data technologies, data ingestion, real-time
processing, and data transformation within a modern cloud
environment.
You
will work closely with data scientists, analysts, and software engineers
to deliver high-quality, secure, and efficient data solutions that drive
business insights and innovation.
Key Responsibilities
- Design and develop data
pipelines, ETL/ELT workflows, and data lake solutions on Azure, AWS, or similar
cloud platforms.
- Build scalable and secure
data architectures supporting analytics, BI, and machine learning use cases.
- Leverage cloud-native data
services such as Azure Data Factory, Microsoft Fabric, Databricks, Synapse
Analytics, or AWS Glue, EMR, Lake Formation, Athena.
- Work with Big Data
frameworks like Apache Spark, Flink, Hive, or Impala to process and analyze
large-scale datasets.
- Implement and manage
real-time streaming solutions using Kafka, Azure Event Hub, AWS Kinesis, or GCP
Pub/Sub.
- Write optimized SQL
queries for data transformation, aggregation, and performance tuning.
- Apply DevOps and CI/CD
practices to automate data pipelines and ensure reliable deployment processes.
- Collaborate across teams
to maintain data quality, security, and governance in all data engineering
workflows.
- Contribute to cloud
infrastructure optimization, ensuring scalability, cost efficiency, and
reliability.
Required Skills and Qualifications
- 4+ years of experience in
data engineering, software development, business intelligence, or data science.
- Proven experience building
and maintaining data lake and big data platforms on Azure or AWS.
- Hands-on knowledge of key
Azure/AWS Data Services, including:
- Azure: Microsoft Fabric,
ADLS Gen2, Azure Data Factory, Databricks, Synapse Serverless/Dedicated SQL
Pool, Azure Functions, Stream Analytics, Data Explorer.
- AWS: S3, Glue, EMR,
Athena, Lake Formation, Lambda, Kinesis.
- Strong understanding of
Big Data technologies such as Apache Spark, Flink, Hive, Impala, HDFS,
Dataproc, EMR, or BigQuery.
- Experience with streaming
and event-driven data pipelines (Kafka, Spark Streaming, Flink, EventHub,
Pub/Sub, Confluent).
- Proficient in SQL for data
analysis, transformations, and troubleshooting.
- Good grasp of cloud
computing, networking, and security within Azure or AWS ecosystems.
- Experience with DevOps,
CI/CD, and Infrastructure-as-Code (IaC) tools in data analytics projects.
- Proficiency in at least
one programming language: Python, Scala, Java, Go, or Rust.
Nice-to-Have Skills
- Strong desire to learn and
implement distributed systems and cloud-native best practices.
- Advanced programming
skills in Python and/or Scala.
- Familiarity with
containerization and orchestration tools such as Docker, Kubernetes, and
ArgoCD.
- Understanding of cloud
storage systems (Azure Data Lake Storage Gen2, GCS, S3).
- Knowledge of observability
and monitoring tools for logging, tracing, and performance optimization.
- Exposure to CI/CD and
automation tools such as Jenkins, GitHub Actions, ArgoCD, Terraform, Helm,
Azure DevOps, or GCP Cloud Build.
Why Join Us
- Work with cutting-edge
cloud and data engineering technologies.
- Collaborate with a
passionate team of data experts, engineers, and architects.
- Continuous opportunities
for professional growth, certifications, and upskilling.
- Be part of innovative
projects leveraging modern cloud-native and big data architectures.