Job
Description
POSITION Senior Data Engineer / Data Engineer LOCATION Bangalore/Mumbai/Kolkata/Gurugram/Hyd/Pune/Chennai EXPERIENCE 2+ Years JOB TITLE: Senior Data Engineer / Data Engineer OVERVIEW OF THE ROLE: As a Data Engineer or Senior Data Engineer, you will be hands-on in architecting, building, and optimizing robust, efficient, and secure data pipelines and platforms that power business-critical analytics and applications. You will play a central role in the implementation and automation of scalable batch and streaming data workflows using modern big data and cloud technologies. Working within cross-functional teams, you will deliver well-engineered, high-quality code and data models, and drive best practices for data reliability, lineage, quality, and security. HASHEDIN BY DELOITTE 2025 Mandatory Skills: Hands-on software coding or scripting for minimum 3 years Experience in product management for at-least 2 years Stakeholder management experience for at-least 3 years Experience in one amongst GCP, AWS or Azure cloud platform Key Responsibilities: Design, build, and optimize scalable data pipelines and ETL/ELT workflows using Spark (Scala/Python), SQL, and orchestration tools (e.g., Apache Airflow, Prefect, Luigi). Implement efficient solutions for high-volume, batch, real-time streaming, and event-driven data processing, leveraging best-in-class patterns and frameworks. Build and maintain data warehouse and lakehouse architectures (e.g., Snowflake, Databricks, Delta Lake, BigQuery, Redshift) to support analytics, data science, and BI workloads. Develop, automate, and monitor Airflow DAGs/jobs on cloud or Kubernetes, following robust deployment and operational practices (CI/CD, containerization, infra-as-code). Write performant, production-grade SQL for complex data aggregation, transformation, and analytics tasks. Ensure data quality, consistency, and governance across the stack, implementing processes for validation, cleansing, anomaly detection, and reconciliation. Collaborate with Data Scientists, Analysts, and DevOps engineers to ingest, structure, and expose structured, semi-structured, and unstructured data for diverse use-cases. Contribute to data modeling, schema design, data partitioning strategies, and ensure adherence to best practices for performance and cost optimization. Implement, document, and extend data lineage, cataloging, and observability through tools such as AWS Glue, Azure Purview, Amundsen, or open-source technologies. Apply and enforce data security, privacy, and compliance requirements (e.g., access control, data masking, retention policies, GDPR/CCPA). Take ownership of end-to-end data pipeline lifecycle: design, development, code reviews, testing, deployment, operational monitoring, and maintenance/troubleshooting. Contribute to frameworks, reusable modules, and automation to improve development efficiency and maintainability of the codebase. Stay abreast of industry trends and emerging technologies, participating in code reviews, technical discussions, and peer mentoring as needed. Skills & Experience: Proficiency with Spark (Python or Scala), SQL, and data pipeline orchestration (Airflow, Prefect, Luigi, or similar). Experience with cloud data ecosystems (AWS, GCP, Azure) and cloud-native services for data processing (Glue, Dataflow, Dataproc, EMR, HDInsight, Synapse, etc.). © HASHEDIN BY DELOITTE 2025 Hands-on development skills in at least one programming language (Python, Scala, or Java preferred); solid knowledge of software engineering best practices (version control, testing, modularity). Deep understanding of batch and streaming architectures (Kafka, Kinesis, Pub/Sub, Flink, Structured Streaming, Spark Streaming). Expertise in data warehouse/lakehouse solutions (Snowflake, Databricks, Delta Lake, BigQuery, Redshift, Synapse) and storage formats (Parquet, ORC, Delta, Iceberg, Avro). Strong SQL development skills for ETL, analytics, and performance optimization. Familiarity with Kubernetes (K8s), containerization (Docker), and deploying data pipelines in distributed/cloud-native environments. Experience with data quality frameworks (Great Expectations, Deequ, or custom validation), monitoring/observability tools, and automated testing. Working knowledge of data modeling (star/snowflake, normalized, denormalized) and metadata/catalog management. Understanding of data security, privacy, and regulatory compliance (access management, PII masking, auditing, GDPR/CCPA/HIPAA). Familiarity with BI or visualization tools (PowerBI, Tableau, Looker, etc.) is an advantage but not core. Previous experience with data migrations, modernization, or refactoring legacy ETL processes to modern cloud architectures is a strong plus. Bonus: Exposure to open-source data tools (dbt, Delta Lake, Apache Iceberg, Amundsen, Great Expectations, etc.) and knowledge of DevOps/MLOps processes. Professional Attributes: Strong analytical and problem-solving skills; attention to detail and commitment to code quality and documentation. Ability to communicate technical designs and issues effectively with team members and stakeholders. Proven self-starter, fast learner, and collaborative team player who thrives in dynamic, fast-paced environments. Passion for mentoring, sharing knowledge, and raising the technical bar for data engineering practices. Desirable Experience: Contributions to open source data engineering/tools communities. Implementing data cataloging, stewardship, and data democratization initiatives. Hands-on work with DataOps/DevOps pipelines for code and data. Knowledge of ML pipeline integration (feature stores, model serving, lineage/monitoring integration) is beneficial. © HASHEDIN BY DELOITTE 2025 EDUCATIONAL QUALIFICATIONS: Bachelor’s or Master’s degree in Computer Science, Data Engineering, Information Systems, or related field (or equivalent experience). Certifications in cloud platforms (AWS, GCP, Azure) and/or data engineering (AWS Data Analytics, GCP Data Engineer, Databricks). Experience working in an Agile environment with exposure to CI/CD, Git, Jira, Confluence, and code review processes. Prior work in highly regulated or large-scale enterprise data environments (finance, healthcare, or similar) is a plus.
