Senior Data Engineer with Cloud & ETL Expertise

KOUSHIK SRIVASTAV LALA

Email: *****************@*****.***

Mobile: 603-***-****

LinkedIn: https://www.linkedin.com/in/koushiksrivastavlala/ Senior Data Engineer

PROFESSIONAL SUMMARY

Data engineer with 4+ years of experience building resilient data pipelines and warehouses using Python, SQL, Spark, Snowflake, Azure and AWS for modern analytics workloads.

Experienced data engineer delivering reliable ETL and ELT solutions on Databricks, Airflow and Snowflake, transforming complex source data into analytics-ready models supporting business stakeholders.

Collaborative contributor partnering with analysts and business teams, translating requirements into scalable data models, Power BI dashboards and Tableau reports for actionable performance insights.

Focused on data quality, governance and performance, optimizing ETL pipelines, monitoring jobs and troubleshooting issues across Hadoop ecosystems, cloud warehouses and real-time streaming platforms.

Guided teams to achieve project milestones resulting in a 30% increase in on-time delivery.

Mentored junior staff leading to a 25% improvement in team skill proficiency and confidence.

Collaborated with cross-functional teams to streamline processes enhancing collaboration and reducing project time by 15%.

TECHNICAL SKILLS

Cloud Platforms - AWS (EC2, Lambda, Glue, S3, Kinesis, IAM, EKS, Redshift), Azure (ADF, Synapse, Azure SQL, Entra ID, Key Vault), GCP (BigQuery, GKE, Cloud Storage), OpenShift

Infrastructure as Code (IaC) - Terraform, Ansible, ARM Templates, Bicep, CloudFormation, Jenkins, Azure DevOps

Monitoring and Incident Response - New Relic, AWS CloudWatch, Azure Monitor, ServiceNow, RCA, SLA Management

Security and Compliance - IAM, Encryption, NIST 800-53, CIS Benchmarks, PCI-DSS, RBAC, Key Vault, Audit Logging

CI/CD and DevOps - Jenkins, GitHub Actions, Git, GitLab, CodePipeline, CI/CD Pipelines, Shell Scripting, automation pipeline management

Programming & Scripting - Python, SQL, Bash, PowerShell, PL-SQL

Data Engineering - AWS Glue, Azure Data Factory, DBT, Apache Kafka, Spark, Hive, GCP Dataflow, ETL tools, data integration pipelines

Databases - Redshift, Snowflake, Azure SQL, PostgreSQL, MongoDB, MySQL

Dashboards and Visualization - Power BI, Tableau, Looker, AWS QuickSight, Tableau Prep

Data Analytics Tools - Alteryx, RapidMiner

Containerization - containers, containerized deployments PROFESSIONAL EXPERIENCE

Northern Trust December 2025 – Present

Senior Data Engineer

Designed end-to-end batch ETL pipelines with Python, SQL and Snowflake on Azure, delivering curated datasets supporting reconciliations and management reporting requirements for stakeholders daily.

Engineered Airflow workflows orchestrating incremental data ingestion from heterogeneous sources into Snowflake, improving refresh frequency, observability, lineage clarity and confidence for downstream analytics stakeholders.

Developed dimensional data models, including Star Schema structures on Snowflake, aligning with business definitions and simplifying self-service Tableau analysis for operational and financial stakeholders.

Optimized complex SQL transformations and Snowflake warehouse configurations, reducing query runtimes, controlling compute consumption and improving responsiveness for interactive business intelligence reports across areas.

Automated data quality validation checks in Python against critical Snowflake tables, detecting anomalies and protecting decision-making for leadership teams and regulatory or audit submissions.

Engineered data preparation, orchestration, integration processes, enhancing data accuracy and consistency by 30%, driving informed decision-making across multiple business units.

Orchestrated workflow automation and architecture design, resulting in a 40% reduction in manual processing time and improved system reliability.

Architected data processing automation and performance optimization strategies, boosting processing efficiency by 50% and ensuring scalable solutions for enterprise-level applications.

Modernized code quality and scalability frameworks, leading to a 25% reduction in technical debt and enhancing system maintainability across diverse platforms.

UnitedHealth Group August 2023 – November 2025

Data Engineer

Integrated streaming ingestion pipelines with Kafka, Spark and Databricks on AWS, consolidating operational feeds into governed datasets supporting timely clinical and operational analytics initiatives.

Configured data lake zones on S3 and Databricks, defining partitioning, retention and access patterns that balanced performance, compliance obligations and downstream self-service analytics requirements.

Implemented dbt transformations and SQL models on Snowflake, stabilizing refresh processes, reducing manual interventions and ensuring accurate metrics across key reporting and analytics artifacts.

Standardized data quality frameworks with Python and SQL, defining validation rules and monitoring checks that improved reliability of curated datasets across healthcare business domains.

Orchestrated cross-platform workflows in Airflow, coordinating dependencies between Snowflake loads, Databricks notebooks and Power BI refreshes to deliver reliable executive dashboards consistently every morning.

Pioneered Operational Insights initiatives, leveraging leadership skills to mentor and guide teams, resulting in a 20% increase in project delivery speed.

Collaborated with PL-SQL and Alteryx experts to develop robust data integration pipelines, increasing data throughput by 45% and reducing data latency.

Revolutionized automation pipeline management using RapidMiner and Tableau Prep, achieving a 35% improvement in data processing speed and accuracy. Tata Consulting Services May 2021 – December 2022

Data Engineer

Analyzed legacy ETL jobs on Hadoop, Hive and Oracle, documenting dependencies and redesigning workflows for migration into modern Spark and Snowflake based data platforms.

Validated complex data migrations from on-premise Teradata environments into BigQuery on GCP, reconciling row counts and business aggregates to ensure reliable ongoing analytics adoption.

Streamlined onboarding of data sources by building reusable ingestion templates in Python and SQL, shortening delivery timelines and improving consistency across engagements and portfolios.

Enhanced reporting capabilities by modeling Data Warehouse structures, aligning dimensional models with KPIs and enabling intuitive dashboards in Tableau and Power BI for leadership.

Established Agile ways of working within distributed data engineering teams, organizing backlogs, ceremonies and work agreements that improved collaboration, overall predictability and stakeholder transparency.

Engineered large-scale architecture initiatives and enterprise rollouts on OpenShift, ensuring containers and containerized deployments improved deployment speed by 50%.

Resolved performance issues in ETL tools, optimizing task dependency tuning and scheduling, leading to a 30% increase in data processing efficiency.

Quantified impact of scrum teams in a shared services environment, implementing enterprise-level governance, resulting in a 15% reduction in project delivery timelines. EDUCATION

Master's in Data Science - Michigan Technological University

Bachelor's in Computer Science and Engineering - Jawaharlal Nehru Technological University

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