Headline

Data Engineer

About

⏱ Immediate joiner – available within 15 days Data Engineer with 3 years of experience building and maintaining production-grade ETL pipelines and cloud data platforms on AWS, processing millions of records daily for enterprise analytics and reporting. Currently working at Infosys, with hands-on experience in PySpark, SQL, Apache Kafka, and Apache Airflow, and AWS services including S3, Glue, Athena, Redshift, and IAM. I specialize in designing scalable data lake architectures, developing efficient Spark transformations, and implementing automated data quality frameworks to ensure reliability, performance, and data accuracy. I also bring strong analytics exposure, working closely with analysts and stakeholders to prepare analytics-ready datasets, perform SQL-based validation, and support dashboards and KPIs. 🔹 Core expertise - ETL / ELT pipelines (batch & streaming) - AWS data lakes (Avro, Parquet, Delta) - Spark optimization & Airflow orchestration - Data quality, reconciliation & monitoring - SQL-based analysis and reporting support 🔹 Open to - Full-time Data Engineer / Data Analyst roles - Freelance / contract projects in: - Data Engineering - Data Analysis - Data Visualization (Power BI) 📩 Contact: apekshaborgaonkar08@gmail.com

Location

Greater Hyderabad Area

Work experience

Data Engineer at Infosys, Dec 2024 - Mar 2026 · 1 yr 4 mos, Bengaluru, Karnataka, India, Built and Maintained ETL pipelines using PySpark, Kafka, and AWS S3 to ingest and process millions of customer support events daily from real-time streams, supporting enterprise-wide reporting and analytics systems for a stakeholder. Designed multi-layer data lake architecture on AWS S3 with raw (Avro), transformed (Parquet), and aggregated reporting layers, implementing partitioning strategies and schema evolution that reduced query latency by 40%. Developed scalable PySpark transformation jobs for data cleansing, deduplication, normalization, joins, and incremental data processing, handling complex business logic across multiple source systems and data formats. Implemented automated data quality framework using PySpark to validate data consistency, completeness, and accuracy across pipeline stages—from source systems through S3 data lake to Oracle databases—reducing production data issues by 60%. Orchestrated complex Apache Airflow DAGs with dependency management, automated retries, SLA monitoring, and failure recovery mechanisms, achieving 99.8% pipeline uptime and maintaining 15-minute data freshness for time-sensitive reporting. Optimized Spark job performance through partition tuning, broadcast joins, caching strategies, and SQL query optimization, reducing processing time and improving resource utilization for large-scale transformations. Configured and managed AWS services including S3 bucket policies, IAM roles, Glue crawlers, and Athena query optimization for secure and efficient data access across multiple teams and applications. Handled late-arriving data scenarios and implemented idempotent pipeline designs with proper checkpointing and state management to ensure data accuracy and prevent duplicate processing in event-driven workflows. Collaborated with cross-functional teams including data analysts, business stakeholders, and DevOps engineers to gather requirements, troubleshoot data issues, and deliver solutions aligned with business objectives.

System Engineer at Infosys, Nov 2022 - Nov 2024 · 2 yrs 1 mo, Banglore

Education

Bachelor of Engineering - BE, Mechanical Engineering, Savitribai Phule Pune University

SSC, SSC, Jawahar Navodaya Vidyalaya - JNV