// Copyright 2020-2023 <NAME>

// This work is licensed under a Creative Commons 
// Attribution-NonCommercial-ShareAlike 4.0 International License.
// Terms - https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode

// GLOBAL STYLING

// using A4 page size and setting a 1.5cm square margin 
#set page(
    paper: "a4",
    margin: (x: 1.5cm, y: 1.5cm),
)

// all links are underlined
#show link: underline

// macro to create an underline below subheadings
// TODO: Need to extend the macro so that subheadings can default with an underline
#let subheadingline() = {
    v(-5pt);
    line(length: 100%);
    v(-5pt)
}

#let sectionseparator() = {
    v(-5pt);
    line(length: 100%);
    v(-5pt)
}

// HEADER

#grid(
    columns: (1fr, 1fr),
    align(left)[
        = <NAME>
        #link("https://www.hrmnjt.dev")
    ],
    align(right)[
        Email: #link("mailto:<EMAIL>") \
        Phone: #link("tel:+971503937005") \
        Github: #link("https://github.com/hrmnjt")[hrmnjt] - 
        Linkedin: #link("https://www.linkedin.com/in/hrmnjt")[hrmnjt]
    ]
)

#align(center)[
    #set par(justify: false)
    *About* - Engineering Manager with a knack for building and scaling robust systems and data products. I'm passionate about open source software, developer experience and systems engineering. Consistently promoted and selected among top 1% performers, resulting in continious development and role expansion.
]

== Experience
#subheadingline()

#grid(
    columns: (1fr, 1fr),
    align(left)[
        *<NAME>uttaim* \
        *#emph("Engineering Manager (Data)")*
    ],
    align(right)[
        Dubai, UAE \
        Sep 2023 - Present
    ]
)
- Created an identity graph for MAFs diverse 65M customer record collected across 8 varied businesses
- Conceptualized and built classification of customers based their interactions with MAF businesses allowing marketing and customer experience teams win their "hearts, minds and wallets"
- Got buy-in from leadership to start a dedicated team which aims to perfect quality, reliability and observability across data, data products and data infrastructure
- *Tech Stack*: Spark, Splink, S3, Vertica, DuckDB, Airflow, Datahub, Kafka, Argo Workflows, Kubernetes, AWS

#grid(
    columns: (1fr, 1fr),
    align(left)[
        *#emph("Engineering Manager (Product)")*
    ],
    align(right)[
        Sep 2021 - Sep 2023
    ]
)
- Led build for central business intelligence platform (internal name: Connect) for MAF which provides insights across market trends, competitive benchmarking and internal business performance
- Led build and integration for internal MarTech and AdTech automation product aiming to improve, consolidate and standardize marketing and advertising workflow across MAF's retail ad network
- *Tech Stack*: Spark, Cube.dev, S3, Vertica, Postgres, Airflow, NodeJS, React, ArgoCD, Kubernetes, AWS
// - Leading a talented team of 10 engineers to self-organize, own and implement an active product roadmap. Ran a Spotify-inspired agile model to arrange team in tribe and squads - 2 squads building a solutions and 3rd squad implementing a commerical bought software
// - Become the bridge between BD/Sales and PDT team to translate requirements into solutions; created a culture of writing technical design docs (called ADRs) and drive implementation within reasonable milestones
// - Balance product delivery with sustaining engineering to ensure code can be maintained over time and iterations. Invested `~`30% average bandwidth on reducing technical debt and improving scalability
// - Be a proponent of open source solutions and convince team to employ learning and practices on observability, orchestration, documentation and security to promote organization culture. Conducted 20+ architecture review and actively participated in `>`70 hiring discussions over last year


#grid(
    columns: (1fr, 1fr),
    align(left)[
        *#emph("Lead Data Engineer")*
    ],
    align(right)[
        Jun 2019 - Sep 2021
    ]
)
- Started as an lead engineer to handle central data infrastructure for MAF i.e. integration with 30+ source/sink, data observability and managing databases, data-lake, reporting and event-bus infrastructure
// only MAF engineer with 4 external vendor DE
// batch data pipelines, data modelling and warehousing, ACL for postgres and vertica, data observability, golden customer record, infrastructure migration and optiomization, data operations
- Led and scaled a team from 5 to 18 engineers over 2 years to create Data Engineering and Operations team backing up the data management and operations required for flagship programs for loyalty, marketing and operations.
// Share program, BAU data operations for C4, Vox, Magic Planet, Ski, Shopping Malls
// Work with MarTech tools - Braze integration, Oneview integration, OneTrust integration
- Led build for an internal product for marketing platform which would crunch customer base of 30M with 600 odd attributes to craft a campaign specific audience with sub-second latency.
- *Tech Stack*: Spark, S3, Vertica, Postgres, HDFS, Airflow, Jenkins, Docker Swarm, AWS
// % Notes to explain this role

// % - Joined MAF as a Senior Data Engineer and at the time MAF Holding was starting to centralize the data infrastructure they had. To understand this better, I should mention that MAF is a AED 18 Bn company operating in 18 countries. It has assets worth AED 63 Bn across Properties, Retail, Leisure & Entertainment and Lifestyle business. Because each OpCo was responsible for their own P&L, each business had different maturity with data and analytics. Holding's role (where I was hired) was to act like a chief architect and improve this maturity for all business.

// % - Started with removing legacy infrastructure. Migrated from HDFS to S3; as nothing else from Hadoop stack was being used. Improved Vertica datawarehouse by fine-tuning netperf, ioperf, sysperf and data within: projections, aggregations. Migrated legacy ETL from Informatica to Airflow. Improved data observability i.e. monitoring and notifications for data. Maintained and built knowledge about Customer Master Data called GCR.

// % - Created a framework with Airflow, Spark and Confluent to orchestrate data pipelines for ingestion, warehousing and analytics. Create loyalty data warehouse i.e. SHARE. Created a ACL for managing access to Vertica. Created integrations for MarTech (Braze Integration, Sendgrid integration, SAP c4c integration, etc.). Created a audience segmentation tool which can compete with Salesforce Marketing cloud but was much faster, cleaner and purpose built for MAF.

// % - XSight had started as a business inside MAF Future Solution and we had conceptualized couple of tools - Dataverse and XConnect. 
// % TODO: Explain XConnect.

#sectionseparator()

#grid(
    columns: (1fr, 1fr),
    align(left)[
        *Mu Sigma Inc.* \
        *#emph("Decision Scientist, Apprentice Leader")*
    ],
    align(right)[
        Bangalore, India \
        May 2015 - Jun 2019
    ]
)
- For leading US Airline, optimized recommender system to create a 30% significant lift in yearly revenue. Worked on reverse engineering performance of real-time recommendations (based on Oracle RTD) used for airlines check-in process
- For leading UK retailer, built data and platform infrastucuture for retail pricing and inventory management.
- For MAF, built backend and data pipelines for internal employee application serving 46K employees with 150+ MAUs
- For MAF, built a configuration based framework for to run data integration and ETL tasks. Enabled 140+ data pipelines from 15 varied data sources to enable BI and analytics use cases

== Education
#subheadingline()

#grid(
    columns: (1fr, 1fr),
    align(left)[
        *PES Institute of Technology* \
        #emph("Bachelor of Engineering - Electrical and Electronics")
    ],
    align(right)[
        Bangalore, India \
        Jun 2011 - Jun 2015
    ]
)

== Skills
#subheadingline()

- *Domains*: Product Engineering, Data Engineering, Machine Learning, Business Intelligence, and DevSecOps
- *Languages*: Proficient - Python, Shell, SQL; Familiar - Scala, Golang, JavaScript
- *Data Stack*: Postgres, SQLite, Vertica, ElasticSearch, DuckDB, Spark, Confluent Kafka, Cube.dev, Trino, Airflow, Superset


// About me

// My full name is <NAME> but almost everyone calls me Harman. On web, you can find me with the handle hrmnjt i.e. my first name without any vowels. I'm originally from Jamshedpur (India) and I moved to Bangalore (India) for college and started my first job in Bangalore. I got a chance to move to Dubai in 2017 for work and post that I've been in Dubai. I consider myself a generalist and have had a diverse experience but my forte is data engineering. When I'm not working I'm reading scrolling twitter, hackernews, reading books; trying out new tech; going on long walks alone or with my wife and dog. 

// About work stuff

// In college I was studying electrical and electronics where I was most fascinated about control systems. I started my career as an intern in a startup which was building a CRM solution of motor industries and it exposed me to world of data science and software engineering. I could draw parallels between control systems and how software systems are created but was more interested learning how to work with data.
// After the internship and completeing college, I joined US based consultancy which specialized in data analytics and data science called Mu Sigma. I got a lot of exposure to work on big data systems with United Airlines, with Tesco and with Majid Al Futtaim. This gave me a chance to learn a lot of data engineering, devops culture, infrastructure, automation, etc.
// I started working with <NAME> as a consultant and later got an offer to join the team directly. Since almost 4 years I've been working with the core data engineering team in MAF. 2 years back I was promoted to Engineering Manager role and have been working on internal products primarily related to digital marketing and advertisement space.

// Explain core data engineering

// Started with upgrading legacy infrastructure. Migrated from HDFS to S3; as nothing else from Hadoop stack was being used. 
// Improved Vertica performance - 2 ways, infra and data model 
//     infra - fine-tuning netperf, ioperf, sysperf, changing node types, rebalancing, dba activities
//     data model - projections, aggregations, incremental data load logic.
// Migrated legacy ETL from Informatica to Airflow
// Improved data observability i.e. monitoring and notifications for data. Maintained and built knowledge about Customer Master Data called GCR.

// Explain DLA

// Internal tool to create audiences and activate them. Audiences are basically customer segments who have satisfy a condition, such as age-range, gender, last_active_30days, did_trx_in_mall, avg_spend etc.
// MAF active customer base = 15M across 16 countries (largest in UAE and KSA around 9M)
// Around 200 attributes for each customer
// 9 Business Units each with 3-4 marketing managers; no synergy between marketing efforts
// consent, duplicate emails, incorrect targetting, no personalization

// Explain Connect

// It solves for many problems that MAF has - Semantic layer for business logic, repository for dashboards and reports, custom dashboards, audience creation activation, running market research (close loop), running ad campaigns (close loop)
// Dashboard shows problem with data say sales is decreasing
// Marketeer can run a market research i.e. survey, see results in a dashboard and understand product needs brand awareness
// Marketeer can run an ad campaign for increasing brand awareness; see results in a dashboard for campaign effectiveness and understand change in sales.