Sajida Shaik

Senior Data Engineer with ~4 years of experience building cloud-native, scalable data platforms for Advanced Driver Assistance Systems (ADAS) and fleet analytics in the automotive industry. Core expertise in Apache Spark, PySpark, AWS (EMR, Lambda, Step Functions, ECS), Python, and end-to-end ETL/ELT pipeline development. Delivered production-grade data systems processing large-scale vehicle sensor data across global NA and EMEA programs.

Strong foundation in data governance, CI/CD automation, and cloud migration, with additional experience applying ML and GenAI to data engineering workflows.

ADAS Data Management Platform (Aug 2022 – Jul 2025)

• Owned 6+ ADAS data applications end-to-end: system design, scalability planning, UAT execution, and production support serving downstream AI, perception, curation, and reprocessing teams
• Built full ingestion-to-storage pipeline for MDF4 vehicle sensor data: parsed and decoded CAN, SomeIP,

Oxford, Ouster, DBC, and ARXML formats; stored processed data as Parquet with session and file metadata tables

• Designed and optimized Apache Spark (Scala/PySpark) pipelines on Amazon EMR, reducing processing latency by 30% and memory usage by 40% through standardized optimization practices
• Resolved critical OOM and memoryOverhead issues in MLDE; refactored for 1 frame/second output, empty

DataFrame handling, and schema mismatch fixes, improving stability and consumer satisfaction

• Migrated legacy Scala pipelines to Python for improved maintainability and AWS service integration;

completed Spark version upgrades across multiple applications to support long-term cloud migration

• Built and configured CI/CD pipelines (Jenkins, GitHub Actions, Docker, Airflow DAG deployment via Ansible,

Terraform); automated REST API and Bash-triggered DAG workflows for MLDE, Data Catalog, and

Reference Data

• Designed PostgreSQL and Elasticsearch schemas enabling faster analytics via Apache Superset and

Kibana; established MapR DB to Superset connectivity via Apache Drill using Docker Compose

• Implemented data lifecycle, retention, and governance for large-scale Hadoop and MapR volumes; enforced

RBAC and secure visualization access governance

CP60 L3 Safety Fleet Data Platform (Parallel , 2023 – Jul 2025)

• Led end-to-end fleet data pipelines for CP60 L3 Safety Fleet across NA and EMEA: ingestion,

transformation, validation, and storage of Gen2/Gen3 vehicle sensor data on AWS

• Designed and maintained AWS infrastructure: EMR (Spark), Airflow, SQS/SNS, DynamoDB, Lambda, and

Step Functions; upgraded Scala versions across all CP60 applications

• Authored and led end-to-end UAT for the entire CP60 data management architecture; updated Auto

Labeling for Gen3 data and integrated with Data Catalog• Deployed DLM code updates to both NA and EMEA EKS clusters; resolved maprcli/hadoop pod environment issues and updated downstream APIs

GenAI Platform & DTC Analytics ( Aug 2025 - Present)

• Designed and built data pipelines and AWS infrastructure (S3, DynamoDB, ECS Fargate, Terraform)

supporting Generative AI tools for internal workflow automation; containerized with Docker and deployed via

GitHub Actions CI/CD

• Built ML models on structured vehicle data to predict next-occurring Diagnostic Trouble Codes (DTCs) within

7 days, enabling proactive fleet maintenance directly on top of existing data platform

• Implemented RAG-based retrieval workflows using

B.Tech in Electronics & Communication Engineering – Lakireddy Bali Reddy College of Engineering, Mylavaram (2018 – 2022)