About Us

Automation Anywhere is a leader in AI-powered process automation that puts AI to work across organizations. The company’s Automation Success Platform is powered with specialized AI, generative AI and offers process discovery, RPA, end-to-end process orchestration, document processing, and analytics, with a security and governance-first approach. Automation Anywhere empowers organizations worldwide to unleash productivity gains, drive innovation, improve customer service and accelerate business growth. The company is guided by its vision to fuel the future of work by unleashing human potential through AI-powered automation. Learn more at www.automationanywhere.com

Key Activities

• Data Pipeline Development: Design, build, and maintain scalable ETL/ELT pipelines for batch and real-time data processing.

• Data Ingestion & Integration: Collect and integrate data from various structured and unstructured sources (APIs, databases, IoT, logs).

• Database & Warehouse Management: Optimize data storage solutions, ensuring efficient querying and retrieval.

• Data Transformation: Implement data cleaning, transformation, and enrichment processes to support analytics and ML workloads.

• Performance Tuning: Optimize data processing performance by improving query execution, indexing, and storage formats.

• Monitoring & Troubleshooting: Identify and resolve pipeline failures, data inconsistencies,and system bottlenecks.

• Data Security & Compliance: Ensure encryption, masking, and governance policies are applied to protect sensitive data.

• Collaboration: Work closely with AI/ML engineers, analysts, and business teams to define data needs and solutions.

• Automation & CI/CD: Implement automated testing, deployment pipelines, and infrastructure-as-code practices for data workflows.

• Documentation: Maintain detailed documentation on data schemas, workflows, and best practices.

• Continuous Learning: Stay updated with new technologies, frameworks, and industry best practices to enhance data engineering capabilities.

Skills & Qualification Criteria

• 5–8 years of hands-on experience in designing, building, and managing scalable data pipelines.

• Proficiency in Python, SQL, or Java for data processing.

• Technical experience in big data, data science, and public cloud

• Strong experience with distributed computing frameworks like Apache Spark, Hadoop or similar frameworks

• Expertise in designing and managing data warehouses using platforms like Redshift, or BigQuery.

• Optimized database queries and stored procedures for improved performance of AWS Batch Jobs.

• Hands-on experience in building and optimizing ETL/ELT pipelines using tools like Apache Airflow, or similar tools

• Experience with AWS (Glue, Redshift, S3), GCP (BigQuery, Dataflow), or Azure. Cloud certifications are a plus.

• Knowledge of real-time data processing tools like Apache Kafka, Flink, or Kinesis.

• Experience with relational (PostgreSQL, MySQL) and NoSQL databases for ex. Dynamodb

• Understanding of data privacy, compliance (GDPR, HIPAA), and best practices for secure data handling.

• Experience with DevOps practices, version control (Git), and infrastructure as code

• Ability to optimize SQL queries, storage formats (Parquet, ORC), and processing frameworks for efficiency.

• Strong ability to work with data scientists, ML engineers, and business teams to meet data needs.