Looking for a skilled BI Developer to join a team in a dynamic, insights-driven role? The focus of this position is to develop, maintain, and enhance reporting solutions that support data-driven decision-making in a complex, high-volume credit environment. This is a hands-on technical role combined with the ability to interpret business requirements and translate them into impactful, user-friendly reports and dashboards. A solid understanding of credit and financial services concepts is essential, ensuring data insights are accurate, relevant, and aligned to business objectives.
A relevant tertiary qualification in Statistics or Actuarial Science
Minimum 3 years’ experience in a BI Developer, BI Analyst, or BI Engineering role.
At least 3-5 years’ experience implementing innovative BI solutions leading to clear business decisions and benefits, including;
At least 3-5 years’ experience in data analysis, including experience in and understanding of database structure design and data extraction principles (ETL).
At least 3-5 years’ experience working with any visualisation tools of which 4 years is on Power BI (DAX, M)
Overall good understanding and application of BI best practice
At least 1 years' experience in Python or R (or any other relevant programming language)
At least 3-5 years' experience in SQL (Postgres, MySql, MS SQL) which involved cleaning, enriching, transforming , data mapping, data aggregation, and data normalization that is suitable for data analysis.
Working with product development life cycle and Agile frameworks
Experience managing own work and that of others and initiatives from start to finish, including planning, scoping, budgeting, and timeline management.
Experience of overseeing and providing technical guidance to team members and colleagues.
Minimum:
Expert in the field of BI development (reports, dashboards, data visualisation, etc.)
Extensive knowledge of BI/reporting and analytical tools
Extensive knowledge of Business Intelligence and Data Warehousing best practices
Extensive knowledge of BI environments, solutions and implementations (end-to-end BI architecture and technologies)
Extensive knowledge in the field of data analysis methodologies (Descriptive-,Diagnostic-,Predictive-,Prescriptive Analytics)
In depth knowledge of both waterfall and agile development approaches
In depth knowledge of SQL query language: Preferably AWS services (Redshift, Athena, DataLake, Python, PostgreSql, MySQL) or MS Technologies (Azure services, SQL, SSAS)
In depth knowledge of design and user experience principles
Good understanding of Agile principles
Ideal:
Extensive knowledge of Power BI services (including a implementation of either of Advanced AI and ML, and Cognitive services within the Power Platform)
Good working knowledge of the AWS ecosystem and services
Experience in analysis/report development in a financial/banking environment
Here’s a comprehensive list of 100 of the most-used formulas in actuarial science, grouped by domain so it’s easy to revise, study, or apply in practice. These formulas cover life contingencies, interest theory, risk models, survival models, loss distributions, credibility, and reserving — everything from exam-level fundamentals to practical applications.
A=P(1+i)nA = P(1+i)^nA=P(1+i)n – Compound amount
P=A(1+i)nP = \frac{A}{(1+i)^n}P=(1+i)nA – Present value
v=11+iv = \frac{1}{1+i}v=1+i1 – Discount factor
d=i1+id = \frac{i}{1+i}d=1+ii – Discount rate
i=d1−di = \frac{d}{1-d}i=1−dd – Interest rate in terms of discount
i(m)=m((1+i)1/m−1)i^{(m)} = m \left( (1+i)^{1/m} - 1 \right)i(m)=m((1+i)1/m−1) – Nominal rate convertible m times per year
(1+i)=(1+d)−1(1+i) = (1+d)^{-1}(1+i)=(1+d)−1 – Relation between interest and discount
An∣i=(1+i)n−1iA_{n|i} = \frac{(1+i)^n - 1}{i}An∣i=i(1+i)n−1 – Accumulated value of annuity-immediate
an∣i=1−vnia_{n|i} = \frac{1 - v^n}{i}an∣i=i1−vn – Present value of annuity-immediate
a¨n∣i=1−vnd\ddot{a}_{n|i} = \frac{1 - v^n}{d}a¨n∣i=d1−vn – Present value of annuity-due
an‾∣=1−vnia_{\overline{n}|} = \frac{1 - v^n}{i}an∣=i1−vn – Annuity-immediate
a¨n‾∣=1−vnd\ddot{a}_{\overline{n}|} = \frac{1 - v^n}{d}a¨n∣=d1−vn – Annuity-due
a∞‾∣=1ia_{\overline{\infty}|} = \frac{1}{i}a∞∣=i1 – Perpetuity-immediate
a¨∞‾∣=1d\ddot{a}_{\overline{\infty}|} = \frac{1}{d}a¨∞∣=d1 – Perpetuity-due
an‾∣(m)=1−vni(m)a_{\overline{n}|}^{(m)} = \frac{1 - v^n}{i^{(m)}}an∣(m)=i(m)1−vn – m-thly annuity-immediate
sn‾∣=(1+i)n−1is_{\overline{n}|} = \frac{(1+i)^n - 1}{i}sn∣=i(1+i)n−1 – Accumulated annuity-immediate
s¨n‾∣=(1+i)n−1d\ddot{s}_{\overline{n}|} = \frac{(1+i)^n - 1}{d}s¨n∣=d(1+i)n−1 – Accumulated annuity-due
Ian‾∣=an‾∣−nvniI_{a_{\overline{n}|}} = \frac{a_{\overline{n}|} - nv^n}{i}Ian∣=ian∣−nvn – Increasing annuity
Ia¨n‾∣=a¨n‾∣−niI_{\ddot{a}_{\overline{n}|}} = \frac{\ddot{a}_{\overline{n}|} - n}{i}Ia¨n∣=ia¨n∣−n – Increasing annuity-due
an‾∣′′=1−(1+i)−nia_{\overline{n}|}'' = \frac{1 - (1+i)^{-n}}{i}an∣′′=i1−(1+i)−n – Continuous annuity approximation
lxl_xlx – Number alive at exact age x
dx=lx−lx+1d_x = l_x - l_{x+1}dx=lx−lx+1 – Number dying between ages x and x+1
qx=dxlxq_x = \frac{d_x}{l_x}qx=lxdx – Probability of death within one year
px=1−qxp_x = 1 - q_xpx=1−qx – Probability of survival one year
tpx=lx+tlx{}_tp_x = \frac{l_{x+t}}{l_x}tpx=lxlx+t – t-year survival probability
tqx=1−tpx{}_tq_x = 1 - {}_tp_xtqx=1−tpx – t-year death probability
μx=−ddxlnlx\mu_x = -\frac{d}{dx} \ln l_xμx=−dxdlnlx – Force of mortality
μx=f(x)S(x)\mu_x = \frac{f(x)}{S(x)}μx=S(x)f(x) – Alternative definition (PDF/Survival)
ex=∫0∞tpx dte_x = \int_0^\infty {}_tp_x \, dtex=∫0∞tpxdt – Complete expectation of life
ex∘=12+∑t=1∞tpxe_x^\circ = \frac{1}{2} + \sum_{t=1}^\infty {}_tp_xex∘=21+∑t=1∞tpx – Curtate expectation of life
Ax=E[vKx+1]=∑k=0∞vk+1 kpx qx+kA_x = E[v^{K_x+1}] = \sum_{k=0}^\infty v^{k+1} \, {}_k p_x \, q_{x+k}Ax=E[vKx+1]=∑k=0∞vk+1kpxqx+k – Whole life assurance
Aˉx=E[vT]\bar{A}_x = E[v^T]Aˉx=E[vT] – Continuous whole life assurance
Ax:n‾∣=∑k=0n−1vk+1 kpx qx+kA_{x:\overline{n}|} = \sum_{k=0}^{n-1} v^{k+1} \, {}_k p_x \, q_{x+k}Ax:n∣=∑k=0n−1vk+1kpxqx+k – n-year term assurance
a¨x=∑k=0∞vk kpx\ddot{a}_x = \sum_{k=0}^\infty v^k \, {}_k p_xa¨x=∑k=0∞vkkpx – Whole life annuity-due
ax=∑k=1∞vk kpxa_x = \sum_{k=1}^\infty v^k \, {}_k p_xax=∑k=1∞vkkpx – Whole life annuity-immediate
a¨x:n‾∣=∑k=0n−1vk kpx\ddot{a}_{x:\overline{n}|} = \sum_{k=0}^{n-1} v^k \, {}_k p_xa¨x:n∣=∑k=0n−1vkkpx – Temporary life annuity
aˉx=∫0∞vt tpx dt\bar{a}_x = \int_0^\infty v^t \, {}_tp_x \, dtaˉx=∫0∞vttpxdt – Continuous whole life annuity
Ax+i a¨x=1A_x + i \, \ddot{a}_x = 1Ax+ia¨x=1 – Fundamental insurance–annuity relation
Ax:n‾∣=1−(1+i)−n npxA_{x:\overline{n}|} = 1 - (1+i)^{-n} \, {}_np_xAx:n∣=1−(1+i)−nnpx – Simplified term insurance (approx.)
nEx=vn npx{}_nE_x = v^n \, {}_np_xnEx=vnnpx – Pure endowment
Axy=AxAyA_{xy} = A_x A_yAxy=AxAy – Joint-life (independent lives)
axy=axaya_{xy} = a_x a_yaxy=axay – Joint-life annuity (independent)
Axy‾=1−(1−Ax)(1−Ay)A_{\overline{xy}} = 1 - (1-A_x)(1-A_y)Axy=1−(1−Ax)(1−Ay) – Last survivor
axy‾=ax+ay−axya_{\overline{xy}} = a_x + a_y - a_{xy}axy=ax+ay−axy – Last survivor annuity
qxy=1−pxpyq_{xy} = 1 - p_x p_yqxy=1−pxpy – Joint probability of at least one death
E(X)=∫0∞xf(x) dxE(X) = \int_0^\infty x f(x)\, dxE(X)=∫0∞xf(x)dx
Var(X)=E[X2]−(E[X])2Var(X) = E[X^2] - (E[X])^2Var(X)=E[X2]−(E[X])2
S(x)=1−F(x)S(x) = 1 - F(x)S(x)=1−F(x) – Survival function
f(x)=ddxF(x)f(x) = \frac{d}{dx}F(x)f(x)=dxdF(x) – PDF
μ(x)=f(x)S(x)\mu(x) = \frac{f(x)}{S(x)}μ(x)=S(x)f(x) – Hazard rate
E[min(X,d)]=∫0dS(x) dxE[\min(X,d)] = \int_0^d S(x)\, dxE[min(X,d)]=∫0dS(x)dx – Limited expected value
Var[min(X,d)]=E[min(X,d)2]−(E[min(X,d)])2Var[\min(X,d)] = E[\min(X,d)^2] - (E[\min(X,d)])^2Var[min(X,d)]=E[min(X,d)2]−(E[min(X,d)])2
E[N]=λE[N] = \lambdaE[N]=λ for Poisson claim count
Var(N)=λVar(N) = \lambdaVar(N)=λ for Poisson
E(S)=E[N]E[X]E(S) = E[N]E[X]E(S)=E[N]E[X] – Compound sum mean
Var(S)=E[N]Var(X)+Var(N)(E[X])2Var(S) = E[N]Var(X) + Var(N)(E[X])^2Var(S)=E[N]Var(X)+Var(N)(E[X])2 – Compound sum variance
MX(t)=E[etX]M_X(t) = E[e^{tX}]MX(t)=E[etX] – Moment generating function
MS(t)=MN(lnMX(t))M_S(t) = M_N(\ln M_X(t))MS(t)=MN(lnMX(t)) – MGF of compound sum
P(X>d)=S(d)P(X > d) = S(d)P(X>d)=S(d) – Tail probability
Value-at-Risk=F−1(p)Value\text{-}at\text{-}Risk = F^{-1}(p)Value-at-Risk=F−1(p) – Quantile at level p
Z=nn+kZ = \frac{n}{n + k}Z=n+kn – Bühlmann credibility factor
X^=ZXˉ+(1−Z)m\hat{X} = Z\bar{X} + (1-Z)mX^=ZXˉ+(1−Z)m – Credibility premium
k=EPVVHMk = \frac{EPV}{VHM}k=VHMEPV – Ratio of expected process variance to variance of hypothetical means
EPV=E[Var(X∣Θ)]EPV = E[Var(X|\Theta)]EPV=E[Var(X∣Θ)]
VHM=Var(E[X∣Θ])VHM = Var(E[X|\Theta])VHM=Var(E[X∣Θ])
Var(Xˉ)=EPVn+VHMVar(\bar{X}) = \frac{EPV}{n} + VHMVar(Xˉ)=nEPV+VHM – Total variance of mean
Z=nVHMnVHM+EPVZ = \frac{nVHM}{nVHM + EPV}Z=nVHM+EPVnVHM – Alternate credibility form
Limited fluctuation credibility: Z=min(1,nn0)\text{Limited fluctuation credibility: } Z = \min(1, \frac{n}{n_0})Limited fluctuation credibility: Z=min(1,n0n)
n0=(z1−α/2EPVrm)2n_0 = \left(\frac{z_{1-\alpha/2} \sqrt{EPV}}{r m}\right)^2n0=(rmz1−α/2EPV)2 – Full credibility standard
Bayesian posterior mean: θ^=nXˉ/σ2+μ/τ2n/σ2+1/τ2\text{Bayesian posterior mean: } \hat{\theta} = \frac{n\bar{X}/\sigma^2 + \mu/\tau^2}{n/\sigma^2 + 1/\tau^2}Bayesian posterior mean: θ^=n/σ2+1/τ2nXˉ/σ2+μ/τ2
Chain Ladder Factor: fj=∑iCi,j+1∑iCi,j\text{Chain Ladder Factor: } f_j = \frac{\sum_i C_{i,j+1}}{\sum_i C_{i,j}}Chain Ladder Factor: fj=∑iCi,j∑iCi,j+1
Ci,j=Cumulative claims for accident year i at development jC_{i,j} = \text{Cumulative claims for accident year } i \text{ at development } jCi,j=Cumulative claims for accident year i at development j
Projected Ci,j+1=Ci,j×fj\text{Projected } C_{i,j+1} = C_{i,j} \times f_jProjected Ci,j+1=Ci,j×fj
Reserve =∑(Ultimate claims−Paid claims)\text{Reserve } = \sum ( \text{Ultimate claims} - \text{Paid claims} )Reserve =∑(Ultimate claims−Paid claims)
Bornhuetter-Ferguson: R=(1−percent reported)×E[Ultimate claims]\text{Bornhuetter-Ferguson: } R = (1 - \text{percent reported}) \times E[\text{Ultimate claims}]Bornhuetter-Ferguson: R=(1−percent reported)×E[Ultimate claims]
Loss ratio method: Expected claims=Premium×Expected LR\text{Loss ratio method: } \text{Expected claims} = \text{Premium} \times \text{Expected LR}Loss ratio method: Expected claims=Premium×Expected LR
Link ratio method: Ultimates=Cumulative×LDF\text{Link ratio method: } \text{Ultimates} = \text{Cumulative} \times \text{LDF}Link ratio method: Ultimates=Cumulative×LDF
Average cost per claim=Total incurredNumber of claims\text{Average cost per claim} = \frac{\text{Total incurred}}{\text{Number of claims}}Average cost per claim=Number of claimsTotal incurred
Claims incurred but not reported (IBNR)=Ultimate−Reported\text{Claims incurred but not reported (IBNR)} = \text{Ultimate} - \text{Reported}Claims incurred but not reported (IBNR)=Ultimate−Reported
Reserve adequacy=ReserveExpected reserve\text{Reserve adequacy} = \frac{\text{Reserve}}{\text{Expected reserve}}Reserve adequacy=Expected reserveReserve
T=future lifetime random variableT = \text{future lifetime random variable}T=future lifetime random variable
S(t)=P(T>t)S(t) = P(T>t)S(t)=P(T>t)
F(t)=1−S(t)F(t) = 1 - S(t)F(t)=1−S(t)
f(t)=−S′(t)f(t) = -S'(t)f(t)=−S′(t)
μ(t)=f(t)S(t)\mu(t) = \frac{f(t)}{S(t)}μ(t)=S(t)f(t)
S(t)=e−∫0tμ(x)dxS(t) = e^{-\int_0^t \mu(x)dx}S(t)=e−∫0tμ(x)dx
E[T]=∫0∞S(t) dtE[T] = \int_0^\infty S(t)\,dtE[T]=∫0∞S(t)dt
Var(T)=2∫0∞tS(t) dt−(E[T])2Var(T) = 2\int_0^\infty tS(t)\,dt - (E[T])^2Var(T)=2∫0∞tS(t)dt−(E[T])2
Gompertz law: μx=Bcx\text{Gompertz law: } \mu_x = B c^xGompertz law: μx=Bcx
Makeham law: μx=A+Bcx\text{Makeham law: } \mu_x = A + B c^xMakeham law: μx=A+Bcx
Π=E[X]+λVar(X)\Pi = E[X] + \lambda Var(X)Π=E[X]+λVar(X) – Variance loading principle
Π=(1+θ)E[X]\Pi = (1+\theta)E[X]Π=(1+θ)E[X] – Expected value principle
Π=1αlnE[eαX]\Pi = \frac{1}{\alpha} \ln E[e^{\alpha X}]Π=α1lnE[eαX] – Exponential principle
Π=E[X]+kVar(X)\Pi = E[X] + k \sqrt{Var(X)}Π=E[X]+kVar(X) – Standard deviation principle
Π=E[X∣X<d]+(1+θ)E[X∣X>d]\Pi = E[X | X < d] + (1+\theta)E[X | X > d]Π=E[X∣X<d]+(1+θ)E[X∣X>d] – Limited stop-loss
Loss ratio=ClaimsPremiums\text{Loss ratio} = \frac{\text{Claims}}{\text{Premiums}}Loss ratio=PremiumsClaims
Expense ratio=ExpensesPremiums\text{Expense ratio} = \frac{\text{Expenses}}{\text{Premiums}}Expense ratio=PremiumsExpenses
Combined ratio=Loss ratio+Expense ratio\text{Combined ratio} = \text{Loss ratio} + \text{Expense ratio}Combined ratio=Loss ratio+Expense ratio
Profit margin=1−Combined ratio\text{Profit margin} = 1 - \text{Combined ratio}Profit margin=1−Combined ratio
Risk-adjusted return=E[Profit]Var(Profit)\text{Risk-adjusted return} = \frac{E[\text{Profit}]}{Var(\text{Profit})}Risk-adjusted return=Var(Profit)E[Profit]
ETL (Extract, Transform, Load) is the backbone of data analysis and Business Intelligence (BI).
Here’s a detailed step-by-step breakdown of how to perform ETL, including concepts, tools, examples, and best practices — the kind of knowledge used by data engineers, analysts, and BI developers.
Stage
Meaning
Goal
E – Extract
Pull data from multiple sources
Gather raw data
T – Transform
Clean, structure, and enrich it
Make it usable
L – Load
Store it in a target system (like a data warehouse)
Make it accessible for analysis
This step gathers data from diverse sources — structured or unstructured.
Common data sources:
Databases (SQL Server, Oracle, MySQL, PostgreSQL)
APIs (Salesforce, Google Analytics, Stripe)
Files (CSV, JSON, XML, Excel)
Cloud storage (AWS S3, Azure Blob, Google Drive)
Logs, sensors, ERP/CRM systems
Key principles:
Maintain data integrity (no missing or duplicated records)
Use incremental extraction where possible (only new or changed data)
Log extraction errors or delays
Tools for extraction:
SQL queries (SELECT * FROM ...)
APIs via Python (e.g., requests, pandas)
ETL tools (e.g., Talend, Informatica, AWS Glue, Fivetran, Airbyte, Apache NiFi)
Transformations clean, standardize, and prepare the data for analytics.
Typical transformations include:
Data cleaning:
Handle missing values (fill, drop, or interpolate)
Remove duplicates
Fix data types (convert strings to dates, etc.)
Data validation:
Check data ranges, referential integrity, and constraints
Data enrichment:
Add calculated columns (e.g., profit = revenue – cost)
Merge with lookup tables
Data structuring:
Normalize or denormalize tables
Create dimensional models (Star/Snowflake schemas)
Business rules:
Convert currencies, apply thresholds, categorize segments
Tools / Methods:
SQL (CTEs, CASE statements, joins)
Python with Pandas or PySpark
Power BI’s Power Query (M Language)
Data warehouse transformations (Snowflake, BigQuery, Redshift)
dbt (data build tool) — great for versioned SQL transformations
Example in Python (Pandas):
import pandas as pd
# Load CSV
df = pd.read_csv("sales.csv")
# Clean data
df.drop_duplicates(inplace=True)
df['date'] = pd.to_datetime(df['date'])
df['profit'] = df['revenue'] - df['cost']
# Enrich
df['year'] = df['date'].dt.year
print(df.head())
Once clean and structured, data is loaded into a target destination — often a data warehouse or data mart for reporting.
Common targets:
Data warehouses: Snowflake, BigQuery, Redshift, Azure Synapse, Teradata
Databases: PostgreSQL, MySQL
BI tools: Power BI, Tableau, Looker (connected via connectors)
Load methods:
Full load: Truncate and reload all data (simple but slow)
Incremental load: Only insert or update new records (efficient)
Upsert: Update existing rows, insert new ones
Batch load: Scheduled at intervals (daily, hourly)
Streaming load: Real-time updates using Kafka, Spark Streaming, or AWS Kinesis
Example:
-- Upsert new data into target table
MERGE INTO target_table t
USING staging_table s
ON t.id = s.id
WHEN MATCHED THEN UPDATE SET t.value = s.value
WHEN NOT MATCHED THEN INSERT (id, value) VALUES (s.id, s.value);
Category
Tools
Best For
Code-based
Python (Pandas, PySpark), SQL, R
Flexible, small/medium data, full control
Open-source ETL
Apache Airflow, Talend, Pentaho, Luigi
Workflow orchestration and scheduling
Cloud-native
AWS Glue, Azure Data Factory, Google Dataflow
Scalable enterprise pipelines
No-code / SaaS
Fivetran, Airbyte, Stitch
Quick setup for data replication
BI-integrated
Power BI Power Query, Tableau Prep
Analyst-friendly, simple transformation
Once loaded, data powers BI dashboards and analytics.
Steps:
Design a data model (fact + dimension tables)
Define KPIs (Key Performance Indicators)
Connect BI tool to the data warehouse
Create dashboards and reports
Automate refreshes (daily/real-time)
Monitor data quality and performance
Example BI stack:
PostgreSQL (raw data) → Airbyte (extract) → dbt (transform) → BigQuery (warehouse) → Power BI (visualize)
Document data sources and transformations
Automate incremental updates
Use version control (Git) for transformation scripts
Apply data validation at each stage
Implement monitoring and alerts for pipeline failures
Ensure data lineage tracking (where data came from and how it changed)
Secure sensitive data (encryption, role-based access)
Maintain metadata and change logs
Optimize queries for performance
Test each stage before deploying to production
Pipeline Example:
[CRM + ERP + API + CSVs]
↓
[Extract: Airbyte]
↓
[Transform: dbt or SQL or PySpark]
↓
[Load: Snowflake or BigQuery]
↓
[BI Layer: Power BI / Tableau / Looker]
Would you like me to give you a practical example project (like “ETL pipeline for a retail sales dashboard”) showing the exact Python + SQL code + data model so you can learn it step-by-step?